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To extract the previously unknown patterns from a large data set is the ultimate goal of any data mining algorithm. Some private or confidential information may be revealed as part of data mining process. In this paper we use min-max normalization approach for preserving privacy during the mining process. We sanitize the original data using min-max normalization approach before publishing. For experimental purpose we have used k-means algorithm and from our results it is evident that our approach preserves both privacy and accuracy.

Keywords

Accuracy, Clustering, K-Means, Min-Max Normalization, Privacy

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References

Rajalaxmi R R, and Natarajan A M (2008). An effective data transformation approach for privacy preserving clustering, Journal of Computer Science, vol 4(4), 320-326.

Karthikeyan B, Manikandan G et al. (2011). A fuzzy based approach for privacy preserving clustering, Journal of Theoretical and applied information Technology, vol 32(2), 118-122.

Manikandan G, Sairam N et al. (2012). Privacy preserving clustering by shearing based data transformation, Proceedings of International Conference on Computing and Control Engineering.

Liu L, Yang K et al. (2012). Using noise addition method based on pre-mining to protect health care privacy, Journal of Control Engineering and Applied Informatics, vol 14(2), 58-64.

Doganay M, Pederson T et al. (2008). Distributed privacy preserving k-means clustering with additive secret sharing, PAIS ‘08 Proceedings of the International Workshop on Privacy and Anonymity in Information Society, 3-11.

Rajalakshmi M, and Purusothaman T (2011). Privacy preserving distributed data mining using randomized site selection, European Journal Of Scientific Research, vol 64(2), 610-624.

Han J, and Kamber M (2006). Data mining-concepts and techniques, 2nd Edn. San Francisco: Morgan Kaufmann Publishers.

Available from http://archive.ics.uci.edu/ml/datasets.html UCI Data Repository.

Effect of Grain Size upon the Thermal Behavior of Copper and Diamond Powders using Differential Scanning Calorimetry (DSC)

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Authors

G. Manikandan ¹, G. Murugan ¹, K. Raghukandan ²

Affiliations
1 Department of Mechanical Engineering, Annamalai University, Annamalai Nagar – 608 002, Tamil Nadu, IN
2 Department of Manufacturing Engineering, Annamalai University, Annamalai Nagar – 608 002, Tamil Nadu, IN

Source

Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:

Abstract

Objectives: In this study, the heat capacity and heat flow of varied grain size of copper and diamond powders were investigated. Methods: Heat capacity and heat flow of varied grain size of copper (1 μm-70 μm) and diamond powders (2 μm-300 μm) were measured using differential scanning calorimetry (DSC). The thermal properties of copper and diamond powders were determined in a temperature range of 0-500oC, with a heating rate of 5k/min in a nitrogen environment. Findings: Heat capacity and heat flow of copper and diamond powders increases with grain size and it is consistent with earlier researchers. Heat capacity of copper and diamond powders also increases with grain size. The heat capacity and heat flow was 1.4 J/gk and 0.24 mW/mg respectively for 60-70μm grain size of copper powder, whereas the values of the same were found to be 0.9 J/gk and 0.4 mW/mg for diamond powder of grain size in the range of 270 to 300 μm is determined. This study reveals that the diamond powders show better thermal performance over copper powder. Application/Improvements: Determination of thermal behavior is critical in determining the amount of heat transferred in micro electronics and solar thermal based applications.

Keywords

Copper, Diamond, Differential Scanning Calorimetry, Grain Size, Thermal Behavior.

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