Algorithms for clustering data
Algorithms for clustering data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Minimum Entropy Clustering and Applications to Gene Expression Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
A Method of Proximity Matrix Based Fuzzy Clustering
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Comparing fuzzy, probabilistic, and possibilistic partitions
IEEE Transactions on Fuzzy Systems
Tuning graded possibilistic clustering by visual stability analysis
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Comparing partitions by means of fuzzy data mining tools
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Refining discretizations of continuous-valued attributes
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
A new index based on sparsity measures for comparing fuzzy partitions
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide the entities into groups or clusters based on their attributes or characteristics. Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. It is akin to defining classes. Since the output of clustering is a partition of the input data, the quality of the partition must be determined as a way of measuring the quality of the partitioning (clustering) process. The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. This paper looks at some commonly used clustering measures including the rand index (RI), adjusted RI (ARI) and the jaccuard index(JI) that are already defined for crisp clustering and extends them to fuzzy clustering measures giving FRI,FARI and FJI. These new indices give the same values as the original indices do in the special case of crisp clustering. The extension is made by first finding equivalent expressions for the parameters, a, b, c, and d of these indices in the case of crisp clustering. A relationship called bonding that describes the degree to which two cluster members are in the same cluster or class is first defined. Through use in crisp clustering and fuzzy clustering the effectiveness of the indices is demonstrated.