cluster analysis

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clus·ter a·nal·y·sis

a set of statistical methods used to group variables or observations into strongly interrelated subgroups.

clus·ter anal·y·sis

(klŭstĕr ă-nali-sis)
Set of statistical methods used to group variablesor observations into strongly interrelated subgroups.

cluster analysis

a statistical method of arranging a set of observations into sub-sets, each of which groups together those observations having similarities.
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References in periodicals archive ?
Livny, "BIRCH: an efficient data clustering method for very large databases," ACM Sigmod Record, vol.
Livny, "BIRCH: A New Data Clustering Algorithm and Its Applications," Data Mining and Knowledge Discovery, vol.
Rajulu, "Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation," in Proceedings of 2014 IEEE International Conference on Computational Intelligent and Computing Research (ICCIC), pp.
Pelillo, Eds., A Comparison of Categorical Attribute Data Clustering Methods, Structural, Syntactic, and Statistical Pattern Recognition, Springer, Berlin, Germany, 2014.
Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, SIAM American Statistical Association, Philadelphia, Pa, USA, 2007.
[58] Presented a comparative study of ant clustering (biologically inspired data clustering)performance relating to other clustering algorithms which are applied to the real-world applications.
Cluster validation is one of the main topics in data clustering; this problem consists in finding and objective criterion to determine how good a partition generated by the clustering algorithm is.
The objective of data clustering is to employ certain clustering algorithms to identify clusters consisting of similar data within a dataset.
Data clustering is the prerequisite for training the ANFIS model and it decides the number of fuzzy rules in the model.
Phase 1 PV Data Clustering. Recently, an algorithm implementing clustering by fast search and find of density peaks (CFSFDP) published on Science is proposed by Rodriguez and Laio [28].
As shown in the previous subsection, data clustering enables us to identify the discrete states ({[q.sub.1], [q.sub.2], ..., [q.sub.m]}) and to label each data point in D with the corresponding discrete state.
Key words: Data clustering, Partitioned-based clustering algorithms, K-means, Initial centroids.