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Articles by T. Velmurugan
Total Records ( 2 ) for T. Velmurugan
  T. Velmurugan and T. Santhanam
  Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging in different groups are dissimilar. Clustering is an unsupervised learning technique. Data clustering is the subject of active research in several fields such as statistics, pattern recognition and machine learning. From a practical perspective clustering plays an outstanding role in data mining applications in many domains. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many areas, for instance marketing, biology, libraries, insurance, city-planning, earthquake studies and www document classification. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems. They are subject of this survey. Also, this survey explores the behavior of some of the partition based clustering algorithms and their basic approaches with experimental results.
  T. Velmurugan and T. Santhanam
  Problem statement: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging to different groups are dissimilar. Clustering is an unsupervised learning technique. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many domains. Approach: In this research, the most representative algorithms K-Means and K-Medoids were examined and analyzed based on their basic approach. The best algorithm in each category was found out based on their performance. The input data points are generated by two ways, one by using normal distribution and another by applying uniform distribution. Results: The randomly distributed data points were taken as input to these algorithms and clusters are found out for each algorithm. The algorithms were implemented using JAVA language and the performance was analyzed based on their clustering quality. The execution time for the algorithms in each category was compared for different runs. The accuracy of the algorithm was investigated during different execution of the program on the input data points. Conclusion: The average time taken by K-Means algorithm is greater than the time taken by K-Medoids algorithm for both the case of normal and uniform distributions. The results proved to be satisfactory.
 
 
 
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