A Comprehensive Survey on Data Using Various Clustering Methodologies
Research Area: | Volume 1 Issue 2, March 2012 | Year: | 2012 |
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Type of Publication: | Article | Keywords: | Clustering, K-Mean, Self-organized map, Hierarchical Clustering, Expectation Maximization |
Authors: |
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Journal: | IJEIR | Volume: | 1 |
Number: | 2 | Pages: | 109-113 |
Month: | March | ||
ISSN: | 2277-5668 | ||
Abstract: | The relational database model is the most
frequently used database model today. With all the strengths
it has, doesn’t perform well with complex queries and as well
as analyzing very large sets of data. As computers have
grown more potent, resulting in the possibility to store very
large data volumes, the need for efficient analysis and
processing of large data sets has emerged. When analyzing
database performance, the disk I/O is a problematic
bottleneck since disk access results in a high latency
compared to memory access. A solution widely used for
minimizing disk access is clustering [7]. The basic principle
of clustering is to organize the data in a way that makes sure
that records that are expected to be queried together are
physically stored together. This means that large blocks of
data can be read once, instead of accessing the disk once for
each record, which would be the case if the data were spread
out. This paper is intended to study the and compare
different clustering techniques used to retrieve the data |
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