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An Analysis on Beta Thalassemia Major Patients through the Techniques of Data Clustering

P.D. Siji, Dr. Vasantha Kalyani David


In data mining, clustering analysis is a technique for grouping data into related component based on similarity metrics. Integration of fuzzy logic with data mining techniques has become one of the key constituents of soft computing. The k means Algorithm is the best method to cluster the crisp data. In traditional clustering algorithm, one object is assigned in to only one cluster. This is valid till the clusters are disjoint and separate. But if the clusters are touching each other or they are overlapping, then one object can belong to more than one cluster. In this case fuzzy clustering comes in to existence. In this paper the grouping of beta thalassemia major disease is taken as a case study. Thalassemia can lead to severe transfusion-dependent anaemia, and it is the most common genetic disorder in all part of the world especially Countries in the Middle East. Fuzzy c means algorithms is applied for the clustering to the database and the result is discussed in this paper.


Beta Thalassemia, Clustering, Fuzzy C Means, K-Means.

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