TITLE:
Classification and Novel Class Detection in Data Streams Using Strings
AUTHORS:
Rimjhim Singh, Manoj B. Chandak
KEYWORDS:
Data Stream, Data Mining, Concept-Drift, Concept-Evolution, Novel, Features
JOURNAL NAME:
Open Access Library Journal,
Vol.2 No.5,
May
28,
2015
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.