CLUSTERING ALGORITHMS - FOR GENE EXPRESSION ANALYSIS

Data Mining refers to as the nontrivial process of “identifying valid, novel, potentially useful and ultimately understandable pattern in data". Based on the type of knowledge that is mined, data mining can be classified in to different models such as Clustering, Decision trees, Association rules, and Sequential pattern and time series. In this paper work, an attempt has been made to study theoretical background and applications of Clustering techniques in data mining with a special emphasis on analysis of Gene Expression under Bioinformatics. Bioinformatics is the study of genetic and other biological information using computer and statistical techniques. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. 1) A good of data means that many of the challenges in biology are now challenges in computing. 2) A step toward addressing this challenge is the use of clustering technique, which is essential in the data mining process to reveal natural structures and identifying interesting patterns in the underlying data. In this paper work, effort has been made to compare between few Clustering algorithms such as : K means, Hierarchical, Self - Organization Map (SOM), and Cluster Affinity Search Technique (CAST) with proposed algorithm called CAST+. Strengths and Weaknesses of the above Clustering algorithms are indented and drawbacks like knowing number of clusters before clustering, and taking affinity threshold as input from the users are rectified by the proposed algorithm. Results show that Proposed Algorithm is efficient in comparison with other Clustering algorithms mentioned above.