PRIVACY PRESERVING CLUSTERING BASED ON LINEAR APPROXIMATION OF FUNCTION
Journal: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY (Vol.12, No. 5)Publication Date: 2014-01-30
Authors : Rajesh Pasupuleti; Narsimha Gugulothu;
Page : 3443-3451
Keywords : Clustering; Privacy preserving; Principal component analysis; Regression; Self organization mapping;
Abstract
Clustering analysis initiatives? a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of the? requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected by? user.? In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields good? results in practice with an example of? business data are provided.? It also? explains privacy preserving clusters of sensitive data objects.
Other Latest Articles
- Social Support and Its Relation To Meaning In Life For Amputees In Gaza Governorates
- CONTROL TRAFFIC TROUGH INDUCTIVE LOOPS IMPLEMENTED AT ROAD INTERSECTION
- DEVELOPMENT OF PUBLIC FINANCIAL CONTROL IN UKRAINE
- Performance Comparison of Hartley Transform with Hartley Wavelet and Hybrid Hartley Wavelet Transforms for Image Data Compression
- Method of Moving Region Detection for Static Camera
Last modified: 2016-06-29 18:16:07