Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering
Pattern Recognition Letters
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
On-Line New Event Detection using Single Pass Clustering TITLE2:
On-Line New Event Detection using Single Pass Clustering TITLE2:
Detecting groups of anomalously similar objects in large data sets
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Gustafson-Kessel algorithm for evolving data stream clustering
CompSysTech '09 Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
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Dynamic clustering with soft computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Applied Soft Computing
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Discovering interesting patterns or substructures in data streams is an important challenge in data mining. Clustering algorithms are very often applied to identify single substructures although they are designed to partition a data set. Another problem of clustering algorithms is that most of them are not designed for data streams. This paper discusses a recently introduced procedure that deals with both problems. The procedure explores ideas from cluster analysis, but was designed to identify single clusters without the necessity to partition the whole data set into clusters. The new extended version of the algorithm is an incremental clustering approach applicable to stream data. It identifies new clusters formed by the incoming data and updates the data space partition. Clustering of artificial and real data sets illustrates the abilities of the proposed method.