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A New Approach for Data Stream Clustering of Arbitrary Shaped Cluster

Esfandani, Gholamreza | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41706 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Abolhassani, Hassan
  7. Abstract:
  8. Recently, data stream has been popular in many contexts like click sequences in web pages, obtained data from sensor networks and satellite data. A data stream is an ordered list of points that should be used once. Clustering of these kinds of data is one of the most difficult issues in data mining. Due to the high amount of data, the traditional clustering algorithms are not suitable for this family of problems. Many data stream clustering algorithms have been proposed in recent years considered the scalability (largeness) of data, but most of them didn’t attend to the following issues. •The quality of clustering can be bad over the time. •Some of the algorithms cannot handle arbitrary shapes of data stream and the results of them are limited to specific regions. Obtaining appropriate clusters for a data stream by handling the arbitrary shapes of clusters is the aim of this project. We proposed an algorithm for data stream clustering and two different algorithms for ordinary data clustering. The overall approach of project is excerpted in two phases. In online phase, the data manipulate with specific data structure called micro cluster. This phase is activated with arrival of data. The offline phase is manually activated by comming request from the use. The phase constructs clusters with the micro clusters made by the online phase. The experimental evaluations shows that proposed algorithms have suitable quality and return good quality even in multi-density environments
  9. Keywords:
  10. Data Mining ; Clustering ; Streaming Algorithm ; Data Stream ; Data Stream Clustering

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