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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2843

Title: An Enhanced Text Mining Approach using Dynamic Programming
Authors: Bisandu, Desmond Bala
Gurumdimma, Nentawe Yusuf
Alams, Mammuam Titus
Datiri, Dachollom Dorcas
Keywords: Algorithms
Data Mining
String Matching
Issue Date: 2018
Publisher: International Journal of Information Systems and Computer Sciences
Series/Report no.: Vol.7;No.5; Pp 34-40
Abstract: Text mining is a pervasive area of research in Computer Science. This is the process of finding knowledgeable and useful information and patterns from very huge text. It is widely applied in several areas of applications such as information retrievals; computational biology etc. Keyword-based searching has been extensively applied in text dataset considering the keywords as strings. String matching is used in finding all the occurrences of a given pattern P of length m from a given text T of length n, where m ≤ n. An occurrence of a pattern inside the text can simply be characterized as “exact” or “approximate”. This paper proposes a framework for text mining using a fast bitparallel algorithm for searching exact occurrences of a pattern inside a huge body of text. We evaluate the performance of three algorithms in the literature on different text files and discuss their suitability under different situations.
URI: http://hdl.handle.net/123456789/2843
ISSN: 2319 – 7595
Appears in Collections:Computer Science

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