Sreedhar Kumar S

Work place: KS School of Engineering and Management /Department of CSE, Bangalore, 560106, India



Research Interests: Bioinformatics, Medical Informatics, Image Compression, Image Processing, Medical Image Computing, Data Mining, Data Structures and Algorithms


Sreedhar Kumar S, received the B.E. degree in Computer Science and Engineering from Bharathidasan University, Tiruchirappalli, Tamilnadu, India in 2000 and the M.E. degree in computer Science and Engineering from Annamallai University, Tamilnadu, India in 2016. He has 14 years’ experience in teaching and research, and currently pursuing Ph.D., in Faculty of Information and Communication Technology from Anna University, Chennai, Tamilnadu, India. Currently he is working as Associate Professor in the Department of Computer Science and Engineering, KS School of Engineering and Management, Bangalore, Karnataka, India, since January 2017. He has published   6 International Journals, 7 International Conferences and 4 National Conferences. His current research includes Data Mining Concepts, Clustering Concepts, Clustering Validation Techniques, Bioinformatics, Image Mining and Medical Image Enhancement.

Author Articles
A New Dynamic Data Cleaning Technique for Improving Incomplete Dataset Consistency

By Sreedhar Kumar S Meenakshi Sundaram S

DOI:, Pub. Date: 8 Sep. 2017

This paper presents a new approach named Dynamic Data Cleaning (DDC) aims to improve incomplete dataset consistency by identifying, reconstructing and removing inconsistent data objects for future data analysis process. The proposed DDC approach consists of three methods:  Identify Normal Object (INO), Reconstruct Normal Object (RNO) and Dataset Quality Measure (DQM).  The first method INO divides the incomplete dataset into normal objects and abnormal objects (outliers) based on degree of missing attributes values in each individual object. Second, the  (RNO) method reconstructs missed attributes values in the normal objects by the closest object based on a distance metric and removes inconsistent data objects (outliers) with higher missed data. Finally, the DQM method measures the consistency and inconsistency among the objects in improved dataset with and without outlier. Experimental results show that the proposed DDC approach is suitable to identify and reconstruct the incomplete data objects for improving dataset consistency from lower to higher level without user knowledge.

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