Algorithms for clustering data
Algorithms for clustering data
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Information Retrieval
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Clustering and Information Retrieval (Network Theory and Applications)
Clustering and Information Retrieval (Network Theory and Applications)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining and Knowledge Discovery
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
A novel document similarity measure based on earth mover's distance
Information Sciences: an International Journal
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
External validation measures for K-means clustering: A data distribution perspective
Expert Systems with Applications: An International Journal
Exploiting noun phrases and semantic relationships for text document clustering
Information Sciences: an International Journal
Adapting the right measures for K-means clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
K-means clustering versus validation measures: a data-distribution perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
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As a widely used clustering validation measure, the F-measure has received increased attention in the field of information retrieval. In this paper, we reveal that the F-measure can lead to biased views as to results of overlapped clusters when it is used for validating the data with different cluster numbers (incremental effect) or different prior probabilities of relevant documents (prior-probability effect). We propose a new ''IMplication Intensity'' (IMI) measure which is based on the F-measure and is developed from a random clustering perspective. In addition, we carefully investigate the properties of IMI. Finally, experimental results on real-world data sets show that IMI significantly alleviates biased incremental and prior-probability effects which are inherent to the F-measure.