Title :
Combining BOW representation and Appriori algorithm for text mining
Author :
Oirrak, A.E. ; Aboutajdine, D.
Author_Institution :
Fac. of Sci. Semlalia, Lab. LISI, Marrakech, Morocco
fDate :
Sept. 30 2010-Oct. 2 2010
Abstract :
The field of text mining seeks to extract useful information from unstructured textual data through the identification and exploration of interesting patterns. The techniques employed usually do not involve deep linguistic analysis or parsing, but rely on simple "Bag-Of-Words" (BPW) text representations based on vector space. In this paper we combine the BOW representation and Appriori algorithm to detect clusters of similar documents and associated rules.
Keywords :
data mining; text analysis; Appriori algorithm; BOW representation; associated rules; bag-of-words representation; text mining; vector space; Association rules; Clustering algorithms; Feature extraction; Itemsets; Semantics; Text mining; Appriori algorithms; Clustering; Text Mining (TM); associated rules; dissimilarity;
Conference_Titel :
I/V Communications and Mobile Network (ISVC), 2010 5th International Symposium on
Conference_Location :
Rabat
Print_ISBN :
978-1-4244-5996-4
DOI :
10.1109/ISVC.2010.5656159