Sharanjit Kaur

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Continually advancing technology has made it feasible to capture data online for onward transmission as a steady flow of newly generated data points, termed as data stream. Continuity and unboundedness of data streams make storage of data and multiple scans of data an impractical proposition for the purpose of knowledge discovery. Need to learn structures(More)
Discovering structures in streaming data is an important data mining task and has motivated design of several well known algorithms. However, in some applications, a higher level of analysis is desirable to reveal the set of dimensions which contribute heavily to the structures. In this paper, we propose an algorithm ISID (Identifying Structures with(More)
Recent advances in the development of data parallel platforms have provided a significant thrust to the development of scalable data mining algorithms for analyzing massive data sets that are now commonplace. Scalable clustering is a common data mining task that finds consequential applications in astronomy, biology, social network analysis and commercial(More)
Recent spurt in research related to scalability of data mining algorithms can be attributed to advances in cloud computing technology, which enables data-intensive applications in distributed environment. Map-Reduce has been the most popular programming paradigm for developing applications in large scale distributed environments. In this paper we present(More)
Revolution in digitized technologies has made it possible to acquire data on-line in the form of data streams, which are continuous and infinite in nature. Multiple applications varying from critical scientific applications to business and financial applications generate transient data. Since streaming data is ordered sequence of continuously growing(More)
Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Several approaches have been proposed for detection of concept drifts in the context of supervised learning in data streams. Recently, researchers have been looking into the problem(More)