Dynamic fuzzy data analysis based on similarity between functions
Fuzzy Sets and Systems
Automatic fault detection in gearboxes by dynamic fuzzy data analysis
Fuzzy Sets and Systems
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Metric Incremental Clustering of Nominal Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Some refinements of rough k-means clustering
Pattern Recognition
Designing evolving user profile in e-CRM with dynamic clustering of Web documents
Data & Knowledge Engineering
Incremental clustering of mixed data based on distance hierarchy
Expert Systems with Applications: An International Journal
Dynamic data assigning assessment clustering of streaming data
Applied Soft Computing
A dynamic data granulation through adjustable fuzzy clustering
Pattern Recognition Letters
Clustering
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Generalized theory of uncertainty (GTU)-principal concepts and ideas
Computational Statistics & Data Analysis
Using Incremental Fuzzy Clustering to Web Usage Mining
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Partially supervised clustering for image segmentation
Pattern Recognition
A class of dynamic rough partitive algorithms
International Journal of Intelligent Systems
Incremental clustering for trajectories
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Dynamic pattern mining: an incremental data clustering approach
Journal on Data Semantics II
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
Dynamic rough clustering and its applications
Applied Soft Computing
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Clustering methods are one of the most popular approaches to data mining. They have been successfully used in virtually any field covering domains such as economics, marketing, bioinformatics, engineering, and many others. The classic cluster algorithms require static data structures. However, there is an increasing need to address changing data patterns. On the one hand, this need is generated by the rapidly growing amount of data that is collected by modern information systems and that has led to an increasing interest in data mining as its whole again. On the other hand, modern economies and markets do not deal with stable settings any longer but are facing the challenge to adapt to constantly changing environments. These include seasonal changes but also long-term trends that structurally change whole economies, wipe out companies that cannot adapt to these trends, and create opportunities for entrepreneurs who establish large multinational corporations virtually out of nothing in just one decade or two. Hence, it is essential for almost any organization to address these changes. Obviously, players that have information on changes first possibly obtain a strategic advantage over their competitors. This has motivated an increasing number of researchers to enrich and extend classic static clustering algorithms by dynamic derivatives. In the past decades, very promising approaches have been suggested; some selected ones will be introduced in this review. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.