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Kavitha, V.
- An Efficient Privacy Preserving Classification Tree Technique in K-Anonymity for Secure Data Mining and Data Publishing
Abstract Views :225 |
PDF Views:2
Authors
Affiliations
1 Computer Science & Engineering Department, Anna University of Technology, Tirunelveli, IN
2 Department of Computer Sci. & Engg, Anna University of Technology, Tirunelveli, IN
1 Computer Science & Engineering Department, Anna University of Technology, Tirunelveli, IN
2 Department of Computer Sci. & Engg, Anna University of Technology, Tirunelveli, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 7 (2011), Pagination: 433-438Abstract
In recent years of data mining applications, an effective technique to preserve privacy is to anonymize the dataset that include private information before being released for mining. Inorder to anonymize the dataset, manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve k-anonymity of a dataset are generalization and suppression. However, generalization presents a major drawback as it requires a manually generated domain hierarchy taxonomy for every quasi identifier in the dataset on which k-anonymity has to be performed. In this paper, new method for achieving k-anonymity based on suppression is proposed. In this method, efficient multi-dimensional suppression is performed, i.e.,values are suppressed only on certain records depending on other attribute values, without the need for manually-produced domain hierarchy trees. Thus, this method identify attributes that have less influence on the classification of the data records and suppress them if needed in order to comply with k-anonymity. The method was evaluated on several datasets to evaluate its accuracy as compared to other k-anonymity based methods. Additionally, a new revised algorithm of kactus called ‘CombS’ can be used.Keywords
Privacy Preserving Data Mining, K-Anonymity, Decision Trees, Classification.
- A Survey on Clustering Analysis
Abstract Views :189 |
PDF Views:6
Authors
C. Mythili
1,
V. Kavitha
2
Affiliations
1 EEE Department, University College of Engineering, Anna University of Technology, Nagercoil Campus, IN
2 University College of Engineering, Anna University of Technology, Nagercoil Campus, IN
1 EEE Department, University College of Engineering, Anna University of Technology, Nagercoil Campus, IN
2 University College of Engineering, Anna University of Technology, Nagercoil Campus, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 10 (2010), Pagination: 321-325Abstract
Cluster analysis is a collection of statistical methods, which identifies group of samples that react similarly or show similar characteristics. The simplest mechanism is to partition the samples using measurements that capture similarity or distance between samples. In this way, clusters and groups are interchangeable words. Often in research studies, cluster analysis is also referred as segmentation method. In neural network concepts, clustering method is called as unsupervised learning. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This paper deals with the survey of cluster analysis.