slugA STUDY ON MBC ALGORITHM WITH GOODNESS FUNCTION
Journal Title: International Journal of Management, IT and Engineering - Year 2011, Vol 1, Issue 3
Abstract
In Data Mining, clustering is one of the efficient techniques used to extract useful information from large quantities of data. A cluster is a collection of data objects relatively similar to one another in some respect and relatively dissimilar to the objects in other clusters. Clustering analysis is an important technique in data mining. It is a process of grouping a set of physical or abstract objects into classes of similar objects. Clustering can be viewed as unsupervised classification. Matrix Based Clustering (MBC) is a hierarchical clustering method with a goodness function based on notions of bond and inner bond that in turn involve direct and indirect link measures. MBC employs an operation close to matrix multiplication, but with a little modification. The matrix base is thought helpful for calculations on advanced parallel machinery. The goal is to achieve good clustering performance relative to other clustering methods. Real data is tested on MBC to provide useful classification information. Matrix Based Clustering algorithm (MBC) measures the “bond" of two clusters based on a goodness function which is computed via matrix manipulation that utilizes not only direct links but also indirect links between two clusters. The effectiveness of MBC is demonstrated with several data sets that contain points in 2D space, a couple of which cannot be captured by other methods such as OPTICS, CHAMELEON, or Matlab Fuzzy Clustering.
Authors and Affiliations
P. Usha Madhuri and Dr. S. P. Rajagopalan
Keywords
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