CLUMP: A Scalable and Robust Framework for Structure Discovery
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An Approach to Identify Unique Styles in Online Handwriting Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Wavelet based approach to cluster analysis. Application on low dimensional data sets
Pattern Recognition Letters
Automatic cluster stopping with criterion functions and the gap statistic
NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Structured large margin machines: sensitive to data distributions
Machine Learning
Clustering decomposed belief functions using generalized weights of conflict
International Journal of Approximate Reasoning
Flexible Grid-Based Clustering
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Identification via location-profiling in GSM networks
Proceedings of the 7th ACM workshop on Privacy in the electronic society
Knee Point Detection in BIC for Detecting the Number of Clusters
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Extraction of Perceptual Hue Feature Set for Color Image/Video Segmentation
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Selecting the "right" number of senses based on clustering criterion functions
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Automated Behavioral Fingerprinting
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
Ontology-Driven Co-clustering of Gene Expression Data
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
A Fast Support Vector Machine Classification Algorithm Based on Karush-Kuhn-Tucker Conditions
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
IPCM separability ratio for supervised feature selection
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Enhanced k-means clustering for patient reported outcome
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
Impact of object extraction methods on classification performance in surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
From baby steps to Leapfrog: how "Less is More" in unsupervised dependency parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Bone segmentation in metacarpophalangeal MR data
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
DHCC: Divisive hierarchical clustering of categorical data
Data Mining and Knowledge Discovery
Classifying services by attributes important to customers
International Journal of Business Intelligence and Data Mining
An optimal cluster-based approach for Subgroup Analysis using Information Complexity Criterion
International Journal of Business Intelligence and Data Mining
Instance-Based parameter tuning via search trajectory similarity clustering
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Predicting web user behavior using learning-based ant colony optimization
Engineering Applications of Artificial Intelligence
Determining the number of clusters with rate-distortion curve modeling
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Evolutionary analysis of functional modules in dynamic PPI networks
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An evolutionary computational model applied to cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
How people describe their place: identifying predominant types of place descriptions
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Automatic clustering of wafer spatial signatures
Proceedings of the 50th Annual Design Automation Conference
Information and Software Technology
Estimating the predominant number of clusters in a dataset
Intelligent Data Analysis
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Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments is specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. In this paper, we investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method that finds the "knee" in a ý# of clusters vs. clustering evaluation metricý graph. Using the knee is well-known, but is not a particularly well-understood method to determine the number of clusters. We explore the feasibility of this method, and attempt to determine in which situations it will and will not work. We also compare the L method to existing methods based on the accuracy of the number of clusters that are determined and efficiency. Our results show favorable performance for these criteria compared to the existing methods that were evaluated.