Classification and Novel Class Detection in Data Streams Using Strings

Rimjhim Singh¹, Manoj B. Chandak²

¹CSE Department, SRCOEM, Nagpur, India.
²CSE Department, Nagpur, India.

DOI: 10.4236/oalib.1101507                

Abstract

Data streams are continuous and always keep evolving in nature. Because of these reasons it becomes difficult to handle such data with simple and static strategies. Data stream poses four main challenges to researchers. These are infinite length, concept-evolution, concept-drift and feature evolution. Infinite-length is because of the amount of data having no bounds. Concept-drift is due to slow changes in the concept of stream. Concept-evolution occurs due to presence of unknown classes in data. Feature-evolution is because of new features continuously keeping appearing in the stream and older ones start disappearing. For performing any analysis on such data we first need to convert it into some knowledgeable form and also need to handle the above mentioned challenges. Various strategies have been proposed to tackle these difficulties. But most of them focus on handling the problem of infinite-length and concept-drift. In this paper, we make efforts to propose a string based strategy to handle infinite-length, concept-evolution and concept-drift.

Keywords

Data Stream, Data Mining, Concept-Drift, Concept-Evolution, Novel, Features

Conflicts of Interest

The authors declare no conflicts of interest.

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