iosr-JCE   Volume-14 ~ Issue-4

Paper Type	:	Research Paper
Title	:	An improved Item-based Maxcover Algorithm to protect Sensitive Patterns in Large Databases
Country	:	India
Authors	:	Mrs. P. Cynthia Selvi, Dr. A. R. Mohamed Shanavas
	:	10.9790/0661-1440105

Abstract: Privacy Preserving Data Mining(PPDM) is a rising field of research in Data Mining and various approaches are being introduced by the researchers. One of the approaches is a sanitization process, that transforms the source database into a modified one by removing selective items so that the counterparts or adversaries cannot extract the hidden patterns from. This study address this concept and proposes a revised Item-based Maxcover Algorithm(IMA) which is aimed at less information loss in the large databases with minimal removal of items.

Keywords: Privacy Preserving Data Mining, Restrictive Patterns, Sensitive Transactions, Maxcover, Sanitized database.

[1] Verykios,V.S, Bertino.E, Fovino.I.N, ProvenzaL.P, Saygin.Y and Theodoridis.Y, "State-of-the-art in Privacy Preservation Data Mining", New York,ACM SIGMOD Record, vol.33, no.2, pp.50-57,2004.

[2] Atallah.M, Bertino.E, Elmagarmid.A, Ibrahim.M and Verykios.V.S, "Disclosure Limitation of Sensitive Rules", In Proc. of IEEE Knowledge and Data Engineering Workshop, pages 45–52, Chicago, Illinois, November 1999.

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[5] Saygin.Y, Verykios.V.S, and Clifton.C, "Using Unknowns to Prevent Discovery of Association Rules", SIGMOD Record, 30(4):45–54, December 2001.

[6] Oliveira.S.R.M and Zaiane.O.R, "Privacy preserving Frequent Itemset Mining", in the Proc. of the IEEE ICDM Workshop on Privacy, Security, and Data Mining, Pages 43-54, Maebashi City, Japan, December 2002.

[7] Oliveira.S.R.M and Zaiane.O.R, "An Efficient One-Scan Sanitization for Improving the Balance between Privacy and Knowledge Discovery", Technical Report TR 03-15, June 2003.

[8] Cynthia Selvi P, Mohamed Shanavas A.R, "An Effective Heuristic Approach for Hiding Sensitive Patterns in Databases", IOSR-Journal on Computer Engineering, Volume 5, Issue 1(Sep-Oct, 2012), PP 06-11, DOI. 10.9790/0661-0510611.

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Paper Type	:	Research Paper
Title	:	Impulsion of Mining Paradigm with Density Based Clustering of Multi Dimensional Spatial Data
Country	:	India
Authors	:	R. Dinesh Sunder, Bobby Lukose
	:	10.9790/0661-1440612

Abstract: Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human's ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The development of clustering algorithms has received a lot of attention in the last few years and new clustering algorithms are proposed. DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data containing noise and outliers. This paper shows the results of analyzing the properties of density based clustering characteristics of three clustering algorithms namely DBSCAN, k-means and SOM using synthetic two dimensional spatial data sets.

Keywords: Clustering, DBSCAN, K-Means, SOM, SOFM

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Paper Type	:	Research Paper
Title	:	Data Classification Algorithm Using k-Nearest Neighbour Method Applied to ECG Data
Country	:	India
Authors	:	Mrs. A. R. Chitupe, Prof. S. A. Joshi
	:	10.9790/0661-1441321

Abstract: In medical science, the importance of the Electrocardiography is remarkable since heart diseases constitute one of the major causes of mortality in the world. Electrocardiogram (ECG) is the only way for doctors to see the cardiac actions of a particular person. It provides a graphic depiction of the electrical forces generated by the heart and then by analysing this graph doctors can tell about any abnormality present in heart. In the paper we focus on the QRS complex detection in electrocardiogram and the idea of further recognition of anomalies in QRS complexes based on some dimensional features of ECG is described. As medical information system is widely used and growing medical databases requires efficient classification method for efficient computer assisted analysis of ECG.

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