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High Dimensional Data Processing in Privacy Preserving Data Mining 9

High Dimensional Data Processing in Privacy Preserving Data ...

引用

IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rathi, Mayur Rajavat, Anand Shri Vaishnav Vidyapeeth Vishwavidyala Dept Comp Sci & Engn Indore India

ISBN: (纸本)9781728149769

In business intelligence data is an essential feature in decision making. An incomplete or lake of information can damage the entire project ideas. Therefore sometimes different business dimensions are collaborating their sensitive and personal data for enhancing decisional ability. During this, the dataset is significantly growing in dimensions. Therefore it is much intense to find a method by which the higher dimensional data can be handled. This paper contributes two key directions of the PPDM (privacy preserving data mining), first a survey conducted on the various PPDM models to understand the working and requirements of the PPDM systems. In addition to an experimental comparative study among PCA, k-PCA and Correlation coefficient based feature selection or dimensionality reduction is conducted. On the basis of experimental observations, the PCA and k-PCA feature selection techniques are degrading the classification accuracy as compared to correlation coefficient based classification. Therefore, in further system design and implementation, the correlation coefficient is used to handling a huge quantity of data dimensions.

关键词： Privacy preserving data mining multi party data source objective summarization model improvements techniques and tools

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High Dimensional Data Processing in Privacy Preserving Data Mining

High Dimensional Data Processing in Privacy Preserving Data ...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Mayur Rathi Anand Rajavat Department of Computer Science & Engineering Shri Vaishnav Vidyapeeth Vishwavidyala Indore India

ISBN: (数字)9781728149769

ISBN: (纸本)9781728149776

In business intelligence data is an essential feature in decision making. An incomplete or lake of information can damage the entire project ideas. Therefore sometimes different business dimensions are collaborating their sensitive and personal data for enhancing decisional ability. During this, the dataset is significantly growing in dimensions. Therefore it is much intense to find a method by which the higher dimensional data can be handled. This paper contributes two key directions of the PPDM (privacy-preserving data mining), first a survey conducted on the various PPDM models to understand the working and requirements of the PPDM systems. In addition to an experimental comparative study among PCA, k-PCA and Correlation coefficient based feature selection or dimensionality reduction is conducted. On the basis of experimental observations, the PCA and k-PCA feature selection techniques are degrading the classification accuracy as compared to correlation coefficient based classification. Therefore, in further system design and implementation, the correlation coefficient is used to handling a huge quantity of data dimensions.

关键词： Privacy preserving data mining multi party data source objective summarization model improvements techniques and tools Data Security dimensional data correlation coefficient (r) Feature extraction personal data data mining

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