A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering
Pattern Recognition Letters
A thresholded fuzzy c-means algorithm for semi-fuzzy clustering
Pattern Recognition
Application of the least trimmed squares technique to prototype-based clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Web Sessions by Sequence Alignment
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbooks of Fuzzy Sets)
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
An new initialization method for fuzzy c-means algorithm
Fuzzy Optimization and Decision Making
Cluster Analysis
Routine classification through sequence alignment
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A Fuzzy Clustering Model for Multivariate Spatial Time Series
Journal of Classification
Comparing fuzzy, probabilistic, and possibilistic partitions
IEEE Transactions on Fuzzy Systems
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
Similarity measures for sequential data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy classification in web usage mining using fuzzy quantifiers
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In the context of human activity pattern analysis, we adopt a fuzzy clustering around medoids approach to classify ordered sequences (paths). These sequences represent patterns of individual behavior in an actual or virtual space-time domain. A fuzzy approach is suitable for path data, since sequences of human activities are typically characterized by switching behaviors, which are likely to produce overlapping clusters. We adopt a partitioning around medoids strategy since in human activity patterns analysis it is useful to represent each cluster by means of an observed (not fictitious) prototype (medoid). To measure pairwise distances among all sequence pairs we make use of the Levenshtein distance, which allows for the comparison between sequences of different length and explicitly takes into account the sequential nature of the data. We also consider two robust versions of the fuzzy clustering algorithm based, respectively, on the noise cluster and on the trimming technique. Robust algorithms deal with noisy observations, which are likely to occur in this framework and could provide an improvement to the standard model. We show several applications on sequence data, regarding different research areas, like Web usage mining, travel behavior, tourists and shopping paths.