A Novel Approach to Extract Best-K Happening Patterns across Streams

Authors

  • K.Nithya  Assistant Professor, Department of Computer Science and Engineering, Nandha College of Technology, Erode-52, Tamil Nadu, India
  • T.Aayebagavathi  UG Students, Department of Computer Science and Engineering, Nandha College of Technology, Erode-52, Tamil Nadu, India
  • V.Mahalakshmi  UG Students, Department of Computer Science and Engineering, Nandha College of Technology, Erode-52, Tamil Nadu, India
  • K.Nithya  UG Students, Department of Computer Science and Engineering, Nandha College of Technology, Erode-52, Tamil Nadu, India
  • K.Vijayalakshmi  UG Students, Department of Computer Science and Engineering, Nandha College of Technology, Erode-52, Tamil Nadu, India

Keywords:

Frequent Pattern Mining, Data Mining, Best-K Happening Patterns

Abstract

Frequent pattern mining is a fundamental problem for many domains, thus has a number of applications. In the Big data and IoT era, objects in these applications are often generated in a streaming fashion. An index-based algorithm is proposed in this project that addresses the challenge and provides the exact answer. The CP-Graph approach, a hybrid index of graph and inverted file structures. The CP-Graph computes the count of a given pattern and updates the answer while pruning unnecessary patterns. Data stream classification has been a widely studied research problem in recent years. The dynamic and evolving nature of data streams requires efficient and effective techniques that are significantly different from static data classification techniques. Two of the most challenging and well studied characteristics of data streams are its infinite length and concept-drift. Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-25

Issue

Volume 4, Issue 1, March-April-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K.Nithya, T.Aayebagavathi, V.Mahalakshmi, K.Nithya, K.Vijayalakshmi, " A Novel Approach to Extract Best-K Happening Patterns across Streams, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.93-97, March-April-2018.