Kalyan Chakravorty: Master Thesis Proposal

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Project Abstract

Historically it has been difficult to measure the deviation in the notion of a concept.The central notion of all these efforts is to detect the change point where the data mining model deviates significantly with respect to the data characteristics that it was trained or built on. The process of detecting such change points is often termed as concept drift. Current state of algorithms (a) assume attribute independence (b) view the problem as a supervised learning problem and need tagged data. The proposed algorithm does not make any assumption among attribute independence and uses the covariance summary to detect concept drift in an unsupervised setting. The algorithm proposed in this thesis monitors the underlying characteristics of the input data, maintains data summaries of the various snapshots in time and develops effective distance metrics to determine when concept drift occurs.We evaluate our technique against synthetic and real data sets

 

 

 

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