Algorithms for clustering data
Algorithms for clustering data
Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Elements of information theory
Elements of information theory
ACM Computing Surveys (CSUR)
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A unified framework for model-based clustering
The Journal of Machine Learning Research
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
K-means clustering versus validation measures: a data distribution perspective
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Model-based evaluation of clustering validation measures
Pattern Recognition
A Generalization of Proximity Functions for K-Means
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
Validation of overlapping clustering: A random clustering perspective
Information Sciences: an International Journal
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Clustering a very large number of textual unstructured customers' reviews in english
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
| Hi-index | 12.05 |
Cluster validation is an important part of any cluster analysis. External measures such as entropy, purity and mutual information are often used to evaluate K-means clustering. However, whether these measures are indeed suitable for K-means clustering remains unknown. Along this line, in this paper, we show that a data distribution view is of great use to selecting the right measures for K-means clustering. Specifically, we first introduce the data distribution view of K-means, and the resultant uniform effect on highly imbalanced data sets. Eight external measures widely used in recent data mining tasks are also collected as candidates for K-means evaluation. Then, we demonstrate that only three measures, namely the variation of information (VI), the van Dongen criterion (VD) and the Mirkin metric (M), can detect the negative uniform effect of K-means in the clustering results. We also provide new normalization schemes for these three measures, i.e., VI"n"o"r"m^', VD"n"o"r"m^' and M"n"o"r"m^', which enables the cross-data comparisons of clustering qualities. Finally, we explore some properties such as the consistency and sensitivity of the three measures, and give some advice on how to use them in K-means practice.