International Journal of Computer Applications Technology and Research, 2014
It is often highly valuable for organizations to have their data analyzed by external agents. Dat... more It is often highly valuable for organizations to have their data analyzed by external agents. Data mining is a technique to analyze and extract useful information from large data sets. In the era of information society, sharing and publishing data has been a common practice for their wealth of opportunities. However, the process of data collection and data distribution may lead to disclosure of their privacy. Privacy is necessary to conceal private information before it is shared, exchanged or published. The privacypreserving data mining (PPDM) has thus has received a significant amount of attention in the research literature in the recent years. Various methods have been proposed to achieve the expected goal. In this paper we have given a brief discussion on different dimensions of classification of privacy preservation techniques. We have also discussed different privacy preservation techniques and their advantages and disadvantages. We also discuss some of the popular data mining algorithms like association rule mining, clustering, decision tree, Bayesian network etc. used to privacy preservation technique.. We also presented few related works in this field.
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019
Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associa... more Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from ...
International Journal of Computer Applications Technology and Research, 2014
It is often highly valuable for organizations to have their data analyzed by external agents. Dat... more It is often highly valuable for organizations to have their data analyzed by external agents. Data mining is a technique to analyze and extract useful information from large data sets. In the era of information society, sharing and publishing data has been a common practice for their wealth of opportunities. However, the process of data collection and data distribution may lead to disclosure of their privacy. Privacy is necessary to conceal private information before it is shared, exchanged or published. The privacypreserving data mining (PPDM) has thus has received a significant amount of attention in the research literature in the recent years. Various methods have been proposed to achieve the expected goal. In this paper we have given a brief discussion on different dimensions of classification of privacy preservation techniques. We have also discussed different privacy preservation techniques and their advantages and disadvantages. We also discuss some of the popular data mining algorithms like association rule mining, clustering, decision tree, Bayesian network etc. used to privacy preservation technique.. We also presented few related works in this field.
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019
Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associa... more Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from ...
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Papers by Dwipen Laskar