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OLS Field Name OLS Field Data
Main Title Interactive pattern mining of neuroscience data /
Author Waranashiwar, Shruti Dilip,
Year Published 2013
OCLC Number 889785107
Subjects Data mining--Research--Methodology--Evaluation. ; Electronic information resource searching. ; Graphic methods--Data processing. ; Software visualization. ; User interfaces (Computer systems) ; Neuroinformatics--Data processing. ; Database searching--Research--Methodology--Evaluation. ; Markov processes. ; Monte Carlo method. ; Statistics--Data processing. ; Life sciences literature--Research. ; Schizophrenia--Data processing. ; Alcoholism--Data processing.
Additional Subjects National Institutes of Health (U.S.).--PubMed Central--Research.
Internet Access
Description Access URL
Library Call Number Additional Info Location Last
ELBM  QP357.5.W37 2013 AWBERC Library/Cincinnati,OH 09/08/2014
Collation viii, 48 leaves : illustrations (some color) ; 28 cm
Cover title. Paper copy printed from online resource. Thesis (M.S.)--Purdue University, 2013. Department of Computer and Information Science, Indiana University-Purdue University Indianapolis (IUPUI). Advisors: Snehasis Mukhopadhyay, Arjan Durresi, Yuni Xia, Shiaofen Fang. Includes bibliographical references (leaves 46-48).
Contents Notes
Text mining is a process of extraction of knowledge from unstructured text documents. We have huge volumes of text documents in digital form. It is impossible to manually extract knowledge from these vast texts. Hence, text mining is used to find useful information from text through the identification and exploration of interesting patterns. The objective of this thesis in text mining area is to find compact but high quality frequent patterns from text documents related to neuroscience field. We try to prove that interactive sampling algorithm is efficient in terms of time when compared with exhaustive methods like FP Growth using RapidMiner tool. Instead of mining all frequent patterns, all of which may not be interesting to user, interactive method to mine only desired and interesting patterns is far better approach in terms of utilization of resources. This is especially observed with large number of keywords. In interactive patterns mining, a user gives feedback on whether a pattern is interesting or not. Using Markov Chain Monte Carlo (MCMC) sampling method, frequent patterns are generated in an interactive way. Thesis discusses extraction of patterns between the keywords related to some of the common disorders in neuroscience in an interactive way. PubMed database and keywords related to schizophrenia and alcoholism are used as inputs. This thesis reveals many associations between the different terms, which are otherwise difficult to understand by reading articles or journals manually. Graphviz tool is used to visualize associations.