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20112

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Author: search.filters.author.Nithya SSubject: search.filters.subject.CLARA

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    AN EFFICIENT CLUSTERING ALGORITHM FOR OUTLIER DETECTION
    (International Journal of Computer Applications, 2011-10) Vijayarani S; Nithya S
    With the help of data mining, an important and valuable knowledge is extracted from the large massive collection of data. There are s several techniques and algorithms are used for extracting the hidden patterns from the large data sets and finding the relationships between them. Clustering is one of the important techniques in data mining. Clustering algorithms are used for grouping the data items based on their similarity. Outlier Detection is a very important research problem in data mining. Clustering algorithms are used for detecting the outliers efficiently. In this research paper, we focused on outlier detection in health data sets such as Pima Indians Diabetes data set and Breast Cancer Wisconsin data set using partitioning clustering algorithms. The algorithms used in this research work are PAM, CLARA AND CLARANS and a new clustering algorithm ECLARANS is proposed for detecting outliers. In order to find the best clustering algorithm for outlier detection several performance measures are used. The experimental results show that the outlier detection accuracy is very good in the proposed ECLARANS clustering algorithm compared to the existing algorithms.
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    SENSITIVE OUTLIER PROTECTION IN PRIVACY PRESERVING DATA MINING
    (International Journal of Computer Applications, 2011-11) Vijayarani S; Nithya S
    Data mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. Privacy preserving data mining is a latest research area in the field of data mining which generally deals with the side effects of the data mining techniques. Privacy is defined as “protecting individual’s information”. Protection of privacy has become an important issue in data mining research. Sensitive outlier protection is novel research in the data mining research field. Clustering is a division of data into groups of similar objects. One of the main tasks in data mining research is Outlier Detection. In data mining, clustering algorithms are used for detecting the outliers efficiently. In this paper we have used four clustering algorithms to detect outliers and also proposed a new privacy technique GAUSSIAN PERTURBATION RANDOM METHOD to protect the sensitive outliers in health data sets.