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Analysis of Different Similarity Functions with Fuzzy C-Means Clustering Approach Using Meeting Transcripts

J.I. Sheeba, K. Vivekanandan


Clustering is a technique of automatically grouping similar data into clusters. A large diversity of similarity measures distance functions such as Euclidean distance, Jaccard distance, Pearson Correlation distance, Cosine similarity and Kullback –Leibler Divergence have been implemented for clustering. Fuzzy C means algorithm is implemented for assigning membership to each word point in the cluster. In the same way it is calculated to each cluster center from the origin of remote region between the cluster center and the word point in this process. This proposed framework is used to validate the five similarity measure functions with Fuzzy C means clustering algorithm for finding the effectiveness. To estimate the optimal number of clusters, by implementing the validity measures like purity and entropy. Finally the results are compared five similarity measure functions with Fuzzy C Means clustering algorithm. Euclidean similarity measure function provides better and accurate results as compared to the other distance functions. nally e � s�o���istical looms to scrutiny because of the hefty amount of aspects, the intricacy of molds or the intricacy in executing the scrutiny. In this paper we will discuss the data extraction in oracle database, oracle data extraction and the algorithm used in the oracle data extraction. The functions of oracle data extraction like directed and undirected sets will be explained using different algorithms.




more �9ei�o���knowledge.The aim of this work is to create a MLPT, to predict Myocardial Infraction. After getting the patient information this MLPT, forecastthat the patient is caused by heart attack or not which is performed by using three Data mining techniques: Naïve Bayes, Decision tree and WAC (Weighted Associative Classifiers). Using the medical prognosis such as chest pain type, thalassic, slope etc., it can predict the probabilities of patients getting a heart disease in the future. The prediction is performed from extracting the patient’s diachronic data or data storage. The research is mainly developed to recover the hidden information from the database. The system has been implemented in JSP and checked using the datasets that is been collected from UCI machine learning repository.



Clustering, Euclidean Distance, Fuzzy C Means Algorithm, Similarity Measure.

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