Information Retrieval (IR) and Extracting Associative Rules

This paper is located in the intersection of two research themes, namely: Information Retrieval and Knowledge Discovery from texts Text mining. The purpose of this paper is two-fold: first, it focuses on Information Retrieval IR whose purpose is to implement a set of models and systems for selecting a set of documents satisfying user needs in terms of information expressed as a query. An information retrieval system is composed mainly of two processes the representation and retrieval process. The process of representation is called indexing, which allows representation of documents and queries by descriptors, or indexes. These descriptors reflect the contents of documents. The retrieval process consists on the comparison between documents representations and query representation. The second aim of this paper is to discover the relationships between terms keywords descriptors of documents in a document database. The correlations relationships between terms are extracted by using a technique of the Text mining, mainly association rules.

[1]  Christian Borgelt,et al.  An implementation of the FP-growth algorithm , 2005 .

[2]  Lynda Tamine Optimisation de requetes dans un Systeme de recherche d'informationapproche basee sur L'exploitation de Techniques Avancees de L'algorithmique Genetique , 2000 .

[3]  Calvin N. Mooers,et al.  Application of random codes to the gathering of statistical information , 1948 .

[4]  Catherine Roussey,et al.  Une mthode d'indexation smantique adapte aux corpus multilingues , 2001 .

[5]  M. E. Maron,et al.  On Relevance, Probabilistic Indexing and Information Retrieval , 1960, JACM.

[6]  S. B. Yahia,et al.  Construction efficace du treillis des motifs fermés fréquents et extraction simultanée des bases génériques de règles , 2011 .

[7]  Xindong Wu,et al.  The Top Ten Algorithms in Data Mining , 2009 .

[8]  Farah Harrathi Extraction de concepts et de relations entre concepts à partir des documents multilingues , 2009 .

[9]  Mustapha Baziz Indexation conceptuelle guidée par ontologie pour la recherche d'information , 2005 .

[10]  Chris Buckley,et al.  New Retrieval Approaches Using SMART: TREC 4 , 1995, TREC.

[11]  Ansaf Salleb Recherche de motifs fréquents pour l'extraction de règles d'association et de caractérisation , 2003 .

[12]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[13]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[14]  Gerard Salton,et al.  A Comparison Between Manual and Automatic Indexing Methods , 1968 .

[15]  Carol L. Barry User-defined relevance criteria: an exploratory study , 1994 .

[16]  Abdullah Saad,et al.  Efficient Implementation of FP Growth Algorithm-Data Mining on Medical Data , 2011 .

[17]  M. F. Porter,et al.  An algorithm for suffix stripping , 1997 .