Algorithms for clustering data
Algorithms for clustering data
OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Applied numerical linear algebra
Applied numerical linear algebra
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
WebACE: a Web agent for document categorization and exploration
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Data clustering analysis in a multidimensional space
Information Sciences: an International Journal
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
A database clustering methodology and tool
Information Sciences—Informatics and Computer Science: An International Journal
Kernel Principle Component Analysis in Pixels Clustering
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Enhancing Data Analysis with Noise Removal
IEEE Transactions on Knowledge and Data Engineering
K-means clustering versus validation measures: a data distribution perspective
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing dimension reduction techniques for document clustering
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validation of overlapping clustering: A random clustering perspective
Information Sciences: an International Journal
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A very important category of clustering methods is hierarchical clustering. There are considerable research efforts which have been focused on algorithm-level improvements of the hierarchical clustering process. In this paper, our goal is to provide a systematic understanding of hierarchical clustering from a data distribution perspective. Specifically, we investigate the issues about how the ''true'' cluster distribution can make impact on the clustering performance, and what is the relationship between hierarchical clustering schemes and validation measures with respect to different data distributions. To this end, we provide an organized study to illustrate these issues. Indeed, one of our key findings reveals that hierarchical clustering tends to produce clusters with high variation on cluster sizes regardless of ''true'' cluster distributions. Also, our results show that F-measure, an external clustering validation measure, has bias towards hierarchical clustering algorithms which tend to increase the variation on cluster sizes. Viewed in light of this, we propose F"n"o"r"m, the normalized version of the F-measure, to solve the cluster validation problem for hierarchical clustering. Experimental results show that F"n"o"r"m is indeed more suitable than the unnormalized F-measure in evaluating the hierarchical clustering results across data sets with different data distributions.