On effective classification of strings with wavelets
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Use of Wavelet Decomposition for String Classification
Data Mining and Knowledge Discovery
Feature-Space Analysis of Unstructured Meshes
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Benchmarking the effectiveness of sequential pattern mining methods
Data & Knowledge Engineering
Data & Knowledge Engineering
CONTOUR: an efficient algorithm for discovering discriminating subsequences
Data Mining and Knowledge Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sequence clustering and labeling for unsupervised query intent discovery
Proceedings of the fifth ACM international conference on Web search and data mining
Simple, fast, and accurate clustering of data sequences
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Hybrid O(n √ n) clustering for sequential web usage mining
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
S2MP: similarity measure for sequential patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Warped K-Means: An algorithm to cluster sequentially-distributed data
Information Sciences: an International Journal
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In recent years, we have seen an enormous growth in the amount of available commercial and scientific data. Data from domains such as protein sequences, retail transactions, intrusion detection, and web-logs have an inherent sequential nature. Clustering of such data sets is usefulfor various purposes. For example, clustering of sequences from commercialdata sets may help marketer identify different customer groups based upon their purchasing patterns. Grouping protein sequences that share similar structure helps in identifying sequences with similar functionality. Over the years, many methods have been developed for clustering objects according to their similarity. However these methods tend to have a computational complexity that is at least quadratic on the number of sequences. In this paperwe present an entirely different approach to sequence clustering that does not require an all-against-all analysis and uses a nearlinear complexity K-means based clustering algorithm. Our experiments using data sets derived from sequences of purchasing transactions and protein sequences show that this approach is scalable and leads to reasonably good clusters.