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K. Dixon, J. Chapman (1980)
Harmonic mean measure of animal activity areasEcology, 61
A New Criterion for Clustering Validity via Contour Approximation of Data
C. Iyigun, Adi Ben-Israel (2008)
PROBABILISTIC DISTANCE CLUSTERING ADJUSTED FOR CLUSTER SIZEProbability in the Engineering and Informational Sciences, 22
G. Rand (1989)
Facilities Location: Models and MethodsJournal of the Operational Research Society, 40
(1988)
Facilities Location: Models and Methods, Amsterdam: North-Holland
(2006)
Introduction to Data Mining , City ? ? ? ? :
M. TEBOULLE (2007)
A Unified Continuous Optimization Framework for Center-Based ClusteringMethodsJournal of Machine Learning, 8
M. Teboulle (2007)
A Unified Continuous Optimization Framework for Center-Based Clustering MethodsJ. Mach. Learn. Res., 8
Anil Jain, R. Dubes (1988)
Algorithms for Clustering Data
A Generalized Weiszfeld Method for Multifacility Location Problems
R. Peters, S. Shanies, J. Peters (1998)
Fuzzy cluster analysis--a new method to predict future cardiac events in patients with positive stress tests.Japanese circulation journal, 62 10
(2005)
Agreement Among Supreme Court Justices: Categorical vs. Continuous Representation
K. Manton, G. Lowrimore, A. Yashin, M. Kovtun (2005)
Fuzzy Cluster Analysis
Marina Arav (2008)
CONTOUR APPROXIMATION OF DATA AND THE HARMONIC MEANJournal of Mathematical Inequalities
M. Shirosaki (1991)
Another proof of the defect relation for moving targetsTohoku Mathematical Journal, 43
Yuzhen Ye (2021)
Clustering AlgorithmsWireless RF Energy Transfer in the Massive IoT Era
H. Kuhn (1973)
A note on Fermat's problemMathematical Programming, 4
Lawrence Ostresh (1978)
On the Convergence of a Class of Iterative Methods for Solving the Weber Location ProblemOper. Res., 26
J. Bezdek (1973)
Fuzzy mathematics in pattern classification
S. Butenko, W. Chaovalitwongse, P. Pardalos (2009)
Clustering challenges in biological networks
(1975)
Clustering Algorithms, New York
Probabilistic Distance Clustering, Theory and Applications
J. Bezdek (1981)
Pattern Recognition with Fuzzy Objective Function Algorithms
(2006)
Introduction to Data Mining, City????: Addison Wesley
(1937)
Sur le point par lequel la somme des distances de n points donnés est minimum
P. Tan, M. Steinbach, Vipin Kumar (2005)
Introduction to Data Mining
(1988)
Algorithms for Clustering Data, New Jersey
R. Love, James Morris, G. Wesolowsky (1988)
FACILITIES LOCATION: MODELS AND METHODS
Probabilistic Semi-Supervised Clustering
W. Heiser (2004)
Geometric representation of association between categoriesPsychometrika, 69
We present a new iterative method for probabilistic clustering of data. Given clusters, their centers and the distances of data points from these centers, the probability of cluster membership at any point is assumed inversely proportional to the distance from (the center of) the cluster in question. This assumption is our working principle.
Journal of Classification – Springer Journals
Published: Jun 18, 2008
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