Abstract
Hindawi Journal of Robotics Volume 2022, Article ID 1007464, 9 pages https://doi.org/10.1155/2022/1007464 Research Article Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining Jintan Zhu School of Electronic Information, Xi’an Railway Vocational and Technical College, Xi’an City, Shaanxi Province 710014, China Correspondence should be addressed to Jintan Zhu; zjt_19810513@163.com Received 15 July 2022; Revised 18 August 2022; Accepted 23 August 2022; Published 16 September 2022 Academic Editor: Shahid Hussain Copyright © 2022 Jintan Zhu. ƒis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the rapid advancement of big data, it is becoming a great problem for people to ‹nd objective information in the database. ƒe relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. As- sociation rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, con‹dence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often di•cult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. ƒe theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge de‹nition method is introduced according to the de‹nition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will ošer a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new de‹nition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. ƒis paper uses two datasets of dišerent sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. ƒe results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy. relationship rules which are unseen from the data will be 1. Introduction analyzed with an ešective rate, what is more, the result of the Since the 21st century, Internet technology and computer old-fashioned data digging skills and it will cause the results hardware technology have developed rapidly and become of massive data because it cannot predict the things hap- popular [1]. ƒe data stock of the Internet has increased pened in the long future [4]. ƒe appearance of the AI will exponentially, but there are only a handful of data con- give more skills to the information-digging process. And the taining valuable information [2]. ƒerefore, mining valuable big process will be the digging of the data information, which information from massive data has changed into the most has created the message of big data [5]. At present, data essential factor or the content in society at present [3]. For mining has an important application value in many aspects. the old and ancient dataset digging information skills, the By describing the existing data, it can ešectively predict the 2 Journal of Robotics future pattern of the data. Data mining technology has 2. State of the Art become a promising research direction in today’s world. 2.1. Data Mining Overview and Research Status. In 1990, the Data mining technology has been applied in scientific re- first KDD International Seminar was held in Detroit, USA. search, production process monitoring, quality manage- Since then, people have shown interest in data mining ment, decision support, and design. terminology [15]. Information is used by people to describe ,e Internet, the Internet of ,ings, cloud computing, specific events in actual society. ,is is an abstract de- and other information technologies are constantly updated, scription of the information. With the development and and they are constantly integrated with the human world in progress of the times, there are more and more aspects of various fields such as economy, politics, military, scientific human exploration, and digital has become a tool to support research, and life [6]. Information visualization is the use of human exploration. Indispensable means that due to the images to express information clearly and effectively. ,e development of science and technology, human beings are image representation can be used to find the original data exploring the physical universe more and more extensively, and the information relationship that cannot be observed. which makes the range of numbers more and more ex- Data visualization can enhance users’ understanding of tensive, and the complexity of data is rapidly increasing [16]. multidimensional and large-scale information and plays an In this case, people can no longer find hidden laws through important role in the discovery, determination, and un- simple logical reasoning. ,erefore, people began to pay derstanding of association rules. As a major information attentiontotheimportanceofdataandeagerlyhopedtofind discovery and pattern recognition technology, the associa- the value andmeaninghidden behind thedata. It is precisely tion processing law of mining data is to find the most to meet this demand that data mining technology was born. meaningful information that can be described. ,e visual- Mining, also known as information mining, is the process of ization of association rules is an inseparable subset of as- discovering potential, meaningful, and interesting things sociation rule theory. Its main task is to display information from a large amount of incomplete and noisy information and help users further grasp the rules of association pro- storedinlargetransactiondatabasesordatawarehouses.,e cessing in order to discover the information results [7]. information mined can often help us to conduct in-depth Dataminingcandiscoverthehiddenlawsinthedataand exploration. Of course, it is not a knowledge exploration effectively exert the value of the data [8]. ,e relevance data method that can retrieve information at any time. ,erefore, processing rule for digging the information can extract through the search engine to find the web pages and search potential and valuable frequent patterns or correlations databases that you are interested in, records cannot become between attributes from the data [9]. Frequent patterns and knowledge discovery. ,ese methods are only looking for correlations can be displayed clearly and intuitively in the information that meets specific conditions, but they do not form of text, but due to the limited cognitive ability of users, explore the things behind big data. Data mining is not a the value of relevant data processing rules for digging the panacea. ,at is,what is foundin largetransaction databases information cannot be fully reflected [10]. ,erefore, it is is not always correct or valuable [17]. ,is needs to meet urgent to study the visualization method of association rules specialcommercialconditions.Peoplestudybusinessanddo in depth, combined with human-computer interaction statistical analysis. Under these premises, the use of infor- technology, to help users analyze and process data resources mation mining is more likely to mine valuable and in- from multiple perspectives, gain insight into valuable in- structive messages [18]. formation, and support their decision-making and planning In the procedure of digging the data will be decided by [11]. requestsfrom businessesanddata features[19].Butitwillbe ,e hypergraphs are widely used in many fields of in- classified as the changing data, preprocessing of data, data formation science. In the past, information visualization mining, and knowledge assessment steps. ,e pre-pro- technology and visual data analysis technology mainly fo- cessing of data will deal with the data and transform it. It cused on analyzing simple binary signals inside data objects costs a lot of time to dealwith thedata [20].,e imageof the [12].However,studieshaveshownthatmultipleassociations data preprocessing process is shown in Figure 1. can more naturally represent the sum pattern of internal Data mining technology originally came from abroad, connections implicit in signals [13]. A Hypergraph is a but its research and development direction are varied. At generalization of ordinary topological relations and can be present, the most common way to deal with analysis easily expressed as multiple relations [14]. ,is also provides problems is decision tree induction. ,e corresponding strong conditions and theoretical support for the visuali- calculation methods are C4. 5 calculation, ID3 algorithm, zation of association rules. ,e hypergraph model combines ID4 algorithm, IDS algorithm, and quest calculation. the characteristics of hypergraphs and multidirectional Complex structured data learning, the slio algorithm, the graphs and can visually describe association rules. In the sprintalgorithm,and“rainforest”calculationsareusedfor graph, nodes represent data items and edges represent as- building a decision tree. Both emphasize the establish- sociation relationships. ,e support and reliability of rules ment of a decision tree with scalability. ,e decision tree can also be described in different ways and with different pruning algorithm includes cost complexity pruning, values. ,erefore, the intuitive display of multirelationships error reduction pruning, and pessimistic evaluation by the hypergraph provides strong theoretical support for pruning. Some methods, such as Bayesian classification, further in-depth research on visualization methods of fre- the back propagation algorithm, the neural network quent item sets and association rules. Journal of Robotics 3 Data cleaning data integration data1 data2 data3 2, null, 4,3,5… 2,3,4,3,5… data Data preprocessing A1 A2 A3 0,3,4,5,10… B1 B2 A2 0,0,3,0,4,0,5,1… B1 data reduction data conversion Figure 1: Data preprocessing process. method, the machine learning method, the CAEP clas- network security technology. ,e visualization technology ofatwo-dimensionalmatrixisusuallyusedtorepresentthe sification method, and the rough set method, are all ap- plied to analysis and data mining. ,ere are also many features on the bar graph. ,e items in the front part and the rear part are arranged on two axes in turn. ,e width data mining techniques and methods to deal with data clustering. Common systems divide clustering methods and color of the bar in turn represent the support and into the k-means method and the basic condensed hier- confidence, as shown in Figure 2. archical clustering method. DBSCAN is a clustering al- gorithm based on density, while optics is a clustering 2.3. Hypergraph Overview. A hypergraph is a subset system algorithm based on density. of a finite set, which is a generalization of graph theory and plays a very important role in discrete mathematics. ,e 2.2. Research Status of Association Rules. Up to now, large- term “hypergraph” was first proposed by Berge in the monograph “Hypergraphs” in 1966. ,e original purpose scale exploration has been carried out in the field of data mining, but the research in the field of data mining is still was to promote some classical results in graph theory. Later, people gradually realized some theorems in graph theory. It popular. ,at is, although the current mining method is quite perfect, its data mining efficiency is very low in the can be generalized in the unified form of a hypergraph, thus face of large-scale information. ,e result is not ideal. opening the prelude to the study of hypergraph theory, ,erefore, the information accumulated by the network making it a huge new branch of graph theory. Compared e-commerce industry is extremely rich and complicated, with the study of general graphs, the study of hypergraphs is resulting in a large number of useless bits of information more complicated. Some important structures and prop- and garbage materials. For such a large amount of infor- erties in general graphs no longer exist in hypergraphs, mation, the preprocessing steps of data mining will be very which complicates the discussion of many similar problems difficult. Once the pretreatment is poor, the whole mining in graph theory. At present, hypergraphs have been widely used in circuit division, knowledge representation and or- process may even fail. At the same time, although the preprocessing process is relatively smooth, conventional ganization methods, cellular communication systems, and mining methods may not obtain valuable and meaningful the representation of molecular structures of atypical data in a large number of databases, even if the mined data compounds and polycyclic conjugated molecules. ,ere are are useless and meaningless. All these show that there are two types of hypergraphs: directed hypergraphs and undi- still many problems in data mining. Considering the dif- rected hypergraphs. Since the 1960s, after decades of un- ferences in the main research directions and mining remitting efforts, the development of hypergraph theory has methods of data mining, there are still many challenging made great progress. research topics in the field of data mining applications. A hypergraph is a binary pair H �(V, E), where ,ese topics are closely linked, mainly involving infor- V � v , v , v , . . . v , denotes the n vertices of the hyper- 1 2 3 n mation fusion technology of information discovery and graph, and E � e , e , e , . . . e , denotes the m hyperedges 1 2 3 n data warehouses, visual data mining, super large-scale data of the hypergraph. A hyperedge set E is a subset defined on a mining of complex types, network data mining, and vertex set V, that is ∀e ⊆V, j � 1,2, . . . m, and satisfies j 4 Journal of Robotics V1 V8 V5 e1 e2 V9 V10 e6 V2 V12 e4 e7 V6 V13 V3 E e5 e3 V11 F H H e8 C V14 V4 V7 Figure 2: Visualization is based on a two-dimensional matrix. Figure 3: Representation of a directed hypergraph H. e ≠∅, j � 1,2, . . . m, A directed hypergraph is H � (V, E), V is the vertex set, and E is the directed hyperedge set. Its adjacency matrix is (1) given as follows: e � V. i�1 a . . . a 11 1n ⎛ ⎜ ⎞ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ,e size of a graph is generally uniquely determined by ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ A � ⎜ ⋮ ⋱ ⋮ ⎟, ⎜ ⎟ ⎝ ⎠ the number of vertices N and the number of super edges M. (3) a . . . a n1 nn In hypergraph, its size also depends on the cardinality of each super edge. We can define the size of a hypergraph as a[i, j] ∈ {0,1,2, . . . . . .}, the cardinality of each hyperedge, and it is given using the aij represents the number of directed hyperedges from the following equation: start point 1 to the end point 1, where v and v are the i i size(H) � e . vertices of H � (V, E). (2) e ∈E Like an undirected hypergraph, the size of a directed hypergraph is defined as the sum of the cardinality of each As an important branch of hypergraph theory, directed super edge, and the rank (low rank) is also defined as the hypergraph theory has not been studied deeply for a long largest (small) multiple of the cardinality of each super edge. time, and the results are relatively small. ,e foreign paper However, it is difficult to form a one-to-one correspondence that has a far-reaching impact on the development of di- between the adjacency matrix and other matrices in a di- rected hypergraph theory is directed hypergraph and its rectedhypergraphduetothecharacteristicsofclustersinthe applicationbyGiorgioGallo.Here,theauthorsystematically hypergraph itself. People can also understand that the di- summarizes the previous achievements in the field of di- rected hypergraph corresponds to the adjacency matrix, but rected hypergraphs. In China, based on the demand for people cannot uniquely recover the original directed electrical engineering and automation research, Professor hypergraph from the adjacency matrix. ,erefore, how to Huang has provided another new way to describe directed reduce thesefactors isalso animportanttopic inthestudy of hypergraphs and has done relevant research work on this directed hypergraph theory. basis. Similar to the theoretical study of undirected graphs and directed graphs, directed hypergraphs add directions to the super edges in undirected hypergraphs, and then de- 3. Methodology scribe the arrangement of the vertices of the super edges. On this basis, we introduce some properties of undirected 3.1. Relevance Data Processing Rule for Digging the Infor- hypergraph into directed hypergraph and find the special mation Process. ,e purpose of a relevance data processing properties of directed hyper-tree. Of course, the main rulefordiggingtheinformationistofindsomecrediblerules purpose of studying directed hypergraphs is to solve from massive data. ,ese rules usually have potential value problems in practical applications. In this paper, people try and significance and can help enterprise managers analyze to describe the relevant laws through the directed hyper- thecurrentmarketsituationandmakecorrectdecisions.,e graph and solve the redundancy and circulation problems in relevance data processing rule for digging the information the relevant data processing rules by using the attributes in systemsearchesforassociationrulesbasedontwominimum the directed hypergraph, and mining information.. Figure 3 thresholds, which are the minimum support threshold min is a representation of a directed hypergraph H. supandtheminimumconfidencethresholdminconf,which Journal of Robotics 5 Table 1: Transaction database D. can usually be specified by the user. ,e relevance data processing rule for digging the information work is mainly TID Phase set dividedintotwostages:(1)Findallitemsetsnotlessthanthe T011 a, b, c minimum support threshold min_sup, that is, frequent sets; T021 a, c, d (2) For each frequent set, find not less than the minimum T031 a, b confidence threshold. Association rules for min_conf. ,e T041 c, d process of mining association rules in stage 2 is as follows: T051 a, b, d ′ ′ there isafrequentset L. Forany proper subset L ⊂ L, L ≠∅ in the itemset L, if Support (L)/Support (L′) 2min_conf, the ′ ′ association rule L ⇒L − L is a credible rule. Table 2: All item sets and support. ,e following uses an example to illustrate the specific Item sets Support (%) process of relevance data processing rules for digging the {a} 88 information. Suppose all items in database D constitute an {b} 65 itemset, and all records in transaction database D are shown {c} 65 in Table 1: {d} 65 Looking at Table 1, it is easy to calculate all 1-, 2-, 3-, and {a, b} 65 4-item sets. ,e four item sets and their corresponding {a, c} 48 support information are shown in Table 2. {a, d} 45 {b, c} 21 Given two thresholds, support threshold min_sup �30% {b, d} 20 and confidence threshold min_conf �70%, then, according {c, d} 45 to Table 2, all frequent sets have {a}, {b}, {c}, {d}, {a, b}, {a, c}, {a, b, c} 25 {a, d},{c, d}.Foreachfrequentsetwhosenumberofitemsets {a, b, d} 25 is greater than 1, association rules can be mined from it. For {b, c, d} 25 example, two rules {a} �{b} and {b}>{a} can be mined from {a, b, c, d} 0 the frequent set {a, b}. Examining each such frequent item set, we can get all the association rules as shown in Table 3: Since the confidence threshold min_conf �70%, according to Table 3, all the strong association rules have Table 3: All association rules. {a} �{b} and {b} �{a}. At this point, the mining of associ- Association rules Confidence (%) ation rules is over. {a} {b} 77 {b} {a} 90 {a} {c} 55 3.2. Apriori Algorithm. ,e Apriori algorithm is the most {c} {a} 65 classic algorithm for relevance data processing rules for {a} {d} 51 diggingtheinformation.,ealgorithmiseasytounderstand {d} {a} 65 intheprincipleofthealgorithm,andconciseandconvenient {c} {d} 65 in the realization of the algorithm. ,e principle of the {d} {c} 66 Apriori algorithm is that the more times the two items appear in pairs in the transaction data, the greater the gets the same rule table that expresses the same meaning. correlation between the two items, and the two items are Such rules will not bring more messages to users or offer any items with a strong correlation. ,e implementation process effective help. In most cases, the number of the redundant of the Apriori algorithm needs to scan the database multiple rule is larger than the meaning rule numbers. times. ,e first scan counts the number of occurrences of each item in the data and deletes some items that do not Redundancyrules can generally include two types: one is subordinate tone rules. For example, the conclusion of rule meettheminimumsupportrequirements.Beforethesecond Xi is consistent with that of XJ, and the premise of Xi meets scan, the items obtained from the first scan should be the sufficient condition for the existence of XJ, that is, XJ is combined in pairs. ,en, the second scan of the data is redundant. ,erefore, repetition rules are also regarded as performed tocount the occurrencesof thecombination, and subordinate rules. Special circumstances. ,e second is the some combinations that do not meet the minimum support repeatedpathprinciple.IfthereareselectorsXiandXJinthe are deleted. After that, the combination and scan are re- rule base at the same time, and there must be two paths peated until no new combination is generated, and the between Xi and XJ, the principle of redundancy can be implementation process of the Apriori algorithm ends. judged. Figure 4 below shows the implementation process of the ,e subordination principle can be expressed by the Apriori algorithm when the minimum support is 0.2. following formula (4): X ⟶ X , 3.3. 3e Redundant Processing Method of Association Rules 2 4 (4) Based on Hypergraph. ,e rule dug up from the relationship X X ⟶ X . 2 3 4 rulewillincludesomeprojectsthatusersdonotneed.Oritis in line with the information that users are familiar with, or it 6 Journal of Robotics Itemset sup Itemset sup Tid Items {A} 0.4 {A} 0.4 10 A,C,D st 1 scan {B} 0.6 {B} 0.6 20 B,C,E {C} 0.6 {C} 0.6 30 A,B,C,E {D} 0.2 {E} 0.6 40 B,E {E} 0.6 (c) (a) (b) Itemset Itemset Sup Itemset Sup {A,B} {A,B} 0.2 {A,C} 0.4 nd L 2 scan {A,C} {A,C} 0.4 {B,C} 0.4 {A,E} {A,E} 0.2 {B,E} 0.6 {B,C} {B,C} 0.4 {C,E} 0.4 {B,E} {B,E} 0.6 (f) {C,E} {C,E} 0.4 (d) (e) Itemset rd 3 scan Itemset Sup {A,B,C} {B,C,E} 0.4 {A,C,E} (h) {B,C,E} (g) Figure 4: Example diagram of the mining process of the Apriori algorithm. ,erepeatingpathrulecanbeexpressedbythefollowing Given a hypergraph is as follows: given formula: H � (X), X ⟶ X X ⟶ X , 1 2 3 4 X � X , X , . . . . . . , X , (8) (5) 1 2 n X ⟶ X ⟶ X . 1 5 4 E � E , E , . . . . . . , E . 1 2 m ,e adjacency matrix in a directed hypergraph com- If H is connected and does not contain any hyperloops, pletely illustrates the adjacency problem between nodes of a then H is called a hyper-tree. graph.Inthedirectedhypergraphbasedonassociationrules, the association between the checked items and the associ- 4. Result Analysis and Discussion ation rules can be expressed by the adjacency matrix. Based on the concept of redundancy rules in circuit science and its 4.1. Model Establishment. In the Markov logic network, this relatedcharacteristics, theredundancycheckingmethodcan paper regards the items in the transaction dataset as nodes be realized on the basis of the directed hypergraph. in the Markov logic network. In this way, the weight be- design G � (V(G), E(G)). (6) tween the two nodes can be regarded as the degree of association between the two items. In the Markov logic Its path is a finite, nonempty sequence, and it is given as network, adding weights to the rules is adopted so that the following: knowledge base of the first-order predicate logic is not so rigid. ,e higher the weight value attached to the rule, the W � v e v e . . . e v . (7) 0 1 1 2 k k greater the restriction on each group in the Markov logic ,at is, the staggered sequence of vertices and edges, network. When the weights of all rules in the knowledge where e ∈ E(G), v ∈ V(G), e are associated with V , v , base are infinite, the Markov logic network is the same as i−1 i j i respectively,1≤ i≤ k,0≤ j≤ kisdenotedas(v , v )path,and the standard one. ,e logical reasoning framework for 0 k order predicates is the same. the vertex v , v is called the starting point and end point of 0 k the path W, respectively. It is called a path v , v , . . . , v is To obtain the parameter value in the data, adjust the 1 2 k−1 the inner vertex of W, k will be named the long W. value of the data. ,e most common way to deal with Fromthegraphtheory,theprocedureofdealingwiththe modulus problems, such as infinite numbers, is the step redundant ones with the hypergraphs into the linking bricks reduction method. In computer mathematics, the ladder and also it will change it into formative tress, because every lowering methods generally include batch ladder lowering, line in the hypergraph represents a linking rule. When the small batch ladder lowering, random gradient lowering, and linking picture will become the formative trees, we need batch ladder lowering. ,e batch ladder lowering method is remove it, and this line is the redundant rule. to calculate the lowest point (or the highest point along the Journal of Robotics 7 0 200 400 600 800 1000 1200 1400 1600 1800 2000 number of iterations Figure 5: Convergence diagram of the Markov logic network model algorithm. Table 4: Section BSAKETS data sheet. Fruit veg Fresh meat Dairy Canned veg Canned meat Frozen meal Beer Wine Softer ink Fish Confectionery 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 1 1 1 0 0 gradient rising trend) along the ladder falling trend during 160000 1200 the calculation process, and the gradient direction change can be obtained by functional derivation. A training set is selected for improvement in each iteration. Different from batch ladder reduction, small batch ladder reduction refers 100000 to selecting local training samples for improvement in each iteration. Random gradient reduction refers to randomly selecting a training sample to improve in each iteration process. 0 200 10000 20000 30000 40000 50000 60000 70000 4.2. Experimental Results and Analysis. Toprovecorrectness, parameters in the Markov logic network framework with Apriori algorithm improved algorithm relationship rules are learned through real data to see if the model converges. ,is paper uses a dataset from a grocery Figure6:Comparisonoftheexecutiontimeoftwoalgorithmswith store to learn the parameters of the Markov logic network different numbers of records. framework model of association rules, and the data set contains 75 variables. ,ere are a total of 3956 pieces of ,is chapter conducts an example analysis of a small information. By using the stochastic gradient descent dataset. ,e data is the data that comes with SPSS Clem- method to learn the parameters of the Markov logic network entine11.1, named BSAKETS1n. ,e data contains 18 fields framework model of association rules, the convergence and 1000 records, mainly including customer information, graph of the Markov logic network framework model of total purchase amount, and purchased items. ,e sighs association rules is obtained. Figure 5 indicates that when include vegetables, fruit, meat, fish, and soft drinks. We use the number reaches 1000, the algorithm begins to converge, part of this data, the purchase item information, for asso- indicating that the one logic net sample of the relationship ciation rule analysis. By processing the data, we get the data rules is converged. in Table 4 (including only part of the processed data). sample mean 8 Journal of Robotics during running exercise,” Biomedical Signal Processing and ,is experiment continues to compare the time effi- Control, vol. 70, no. 5, Article ID 102941, 2021. ciencyof theApriorialgorithmandtheimproved algorithm. [3] H. Li, M. Qiao, and S. Peng, “Research on the recommen- ,e dataset used in the experiment is the basket data dation algorithm of rural tourism routes based on the fusion retail.datfileofaretailstoreinBelgiumfromtheCSDNblog. model of multiple data sources,” Discrete Dynamics in Nature ,e dataset has a total of 88,162 records and 16,470 binary and Society, vol. 2022, no. 11, Article ID 2262148, 125 pages, attributes. ,e experimental test results are shown in Fig- ure 6. As can be seen from the figure, the execution time of [4] S. Maleki, U. Agarwal, and M. Burtscher, “BiPart: a parallel the improved algorithm has been greatly reduced compared and deterministic multilevel hypergraph partitioner,” Inter- with the Apriori algorithm. national Journal of Approximate Reasoning, vol. 10, no. 8, pp. 88–95, 2020. 5. Conclusion [5] S. Bashkari, A. Sami, and M. Rastegar, “Outage cause de- tection in power distribution systems based on data mining,” In today’s big data era, relationship rule mining can be a IEEE Transactions on Industrial Informatics, vol. 11, no. 99, popular investigation direction. More and more people start p. 1, 2020. to study association law and they are employed in many [6] V. S. Klaus, S. C. Schriever, J. M. Monroy Kuhn et al., areas. In the research about association rules, people have “Correlation guided Network Integration (CoNI) reveals obtained a lot of results and gained good consequences. But novel genes affecting hepatic metabolism,” Molecular Meta- there are still some shortcomings. To address these prob- bolism, vol. 53, no. 7747, 2021. lems, this paper proposes a Markov net model with rela- [7] N. Lakshmi and M. Krishnamurthy, “Frequent itemset gen- tionship rules. Most of the current relevance data processing eration using relevance data processing rule for digging the rules for digging up the information algorithms are con- information based on hybrid neural network based billiard structed under a unified framework model. ,e main inspired optimization,” Journal of Circuits, Systems, and Computers, vol. 31, no. 8, pp. 113–121, 2022. contents of this paper include the following aspects: (1) [8] V. Soboleva and O. Shipitko, “Raindrops on windshield: Combining Markov logic network and association rules, a dataset and lightweight gradient-based detection algorithm,” newmodel,theMarkovoneframeworkofrelationshiprules, Springer International Publishing, vol. 6, no. 5, pp. 30–45, will be proposed. (2) Employ the random counting method of the Markov logic network framework. By comparing the [9] N. Veldt, A. Wirth, and D. F. Gleich, “Parameterized ob- prediction accuracy of the Markov logical network frame- jectives and algorithms for clustering bipartite graphs and work model and the traditional relevance data processing hypergraphs,” Pattern Analysis & Applications, vol. 6, no. 21, rule for digging the information, the Apriori algorithm, pp. 77–85, 2020. throughdifferentdatasets,achievedhigheraccuracythanthe [10] Z. An, G. A. Fei, and Y. A. Mi, “A new rule reduction and traditional association rule algorithm (Apriori algorithm). training method for extended belief rule base based on (3) In line with the test about the relationship with the rule DBSCAN algorithm,” International Journal of Approximate redundancy for the hypergraph, the author will change the Reasoning, vol. 119, no. 20, pp. 20–39, 2020. relation rule into the hypergraph and give a new concept to [11] N. Parashar, A. Tiwari, and R. K. Gupta, “Mining minimal thetermaboutthematrixtoofferanewwaytothetestofthe non-redundant image association rules using statistical tex- circle and linking bricks in the hypergraph. ture feature based frequent itemsets,” Evolutionary Intelli- gence, vol. 15, no. 2, pp. 33–41, 2022. [12] S.MudgalandM.Parmar,“Miningofthecorrelationsforfatal Data Availability road accident using graph-based fuzzified FP-growth algo- ,e data used to support the findings of this study are rithm,” Blue Eyes Intelligence Engineering and Sciences En- available from the corresponding author upon request. gineering and Sciences Publication-BEIESP, vol. 10, no. 5, pp. 119–128, 2020. [13] J. Chen, C. Zhao, and Uliji, “Collaborative filtering recom- Conflicts of Interest mendation algorithm based on user correlation and evolu- tionary clustering,” Springer International Publishing, vol. 10, ,e authors declare that there are no conflicts of interest. no. 1, pp. 55–62, 2020. [14] F. Chen, J. Liao, and Z. Lu, “A real-time two-stage and dual- Acknowledgments check template matching algorithm based on normalized cross-correlation for industrial vision positioning,” Pattern ,is work was supported by the Xi’an Railway Vocational Analysis & Applications, vol. 8, no. 8, pp. 64–70, 2021. and Technical College. [15] Y. S. Wu, Y. Shen, and H. Sun, “Intelligent algorithm-based analysis on ultrasound image characteristics of patients with References lower extremity arteriosclerosis occlusion and its correlation with diabetic mellitus foot,” Journal of Healthcare Engineer- [1] S. Doganer and H. E. Akolu, “,e effect of sports science ing, vol. 2021, no. 9, 99 pages, Article ID 7758206, 2021. students’ social media adddictions on redundant purchasing [16] S. Govindarajan, J. A. Ardila-Rey, and K. Krithivasan, “De- behavior,” Asian Journal of Education and Training, vol. 6, velopment of hypergraph based improved random forest no. 4, pp. 24–30, 2020. algorithm for partial discharge pattern classification,” IEEE [2] H. Ge, H. Qin, S. Xue et al., “Research on denoising algorithm of thoracic impedance signal for respiratory monitoring Access, vol. 15, no. 99, p. 1, 2021. Journal of Robotics 9 [17] C. . Robinson, “Goal agnostic planning using maximum likelihood paths in hypergraph world models,” Springer In- ternational Publishing, vol. 9, no. 2, pp. 121–130, 2021. [18] Z.Yang,L.Xu,andL.Zhao,“Multimodalfeaturefusionbased hypergraph learning model,” Computational Intelligence and Neuroscience, vol. 2022, Article ID 9073652, 13 pages, 2022. [19] B.Zhang,L.Wang,andY.Li, “Precisionmarketingmethodof E-commerce platform based on clustering algorithm,” Complexity, vol. 2021, no. 10, Article ID 5538677, 10 pages, [20] J. Fang, “Research on automatic cleaning algorithm of multi- dimensional network redundant data based on big data,” Evolutionary Intelligence, vol. 9, no. 25, pp. 1–9, 2021.
Journal
Journal of Robotics
– Hindawi Publishing Corporation
Published: Sep 16, 2022
