New Similarity Index Based on the Aggregation of Membership Functions Through OWA Operator

• Autorzy: Amine Aïtyounes , Frédéric Blanchard , Michel Herbin
• Języki publikacji: EN

Abstrakty

EN

In the field of data analysis, the use of metrics is a classical way to assess pairwise similarity. Unfortunately the popular distances are often inoperative because of the noise, the multidimensionality and the heterogeneous nature of data. These drawbacks lead us to propose a similarity index based on fuzzy set theory. Each object of the dataset is described with the vector of its fuzzy attributes. Thanks to aggregation operators, the object is fuzzified by using the fuzzy attributes. Thus each object becomes a fuzzy subset within the dataset. The similarity of a reference object compared to another one is assessed through the membership function of the fuzzified reference object and an aggregation method using OWA operator. (original abstract)

Słowa kluczowe

EN

Data analysis; Fuzzy sets; Recommender system

PL

Analiza danych; Zbiory rozmyte; System rekomendujący

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2015
• Tom: 5
• Strony: 163--168

Twórcy

autor

Amine Aïtyounes

• Université de Reims Champagne Ardenne, France

autor

Frédéric Blanchard

• Université de Reims Champagne Ardenne, France

autor

Michel Herbin

• Université de Reims Champagne Ardenne, France

Bibliografia

• Bache, K., Lichman, M., UCI Machine learning repository. http://archive.ics.uci.edu/ml, University of California, Irvine, School of Information and Computer Sciences (2013)
• Barrena, M., Jurado, E., Marquez, P. and Pachon, C., A flexible framework to ease nearest neighbor search in multidimensional data spaces, Data and Knowledge Engineering, 69, pp. 116-136 (2010)
• Bohm, C., Berchtold, S. and Keim, D.A., Searching in high-dimensional spaces: index structures for improving the performance of multi-media databases, ACM Computing Surveys, 33(3), pp. 322-373 (2001)
• de Borda, M., Memoire sur les elections au scrutin, Academie Royale des Sciences, Paris (1784)
• Cha, S.H., Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions, International Journal of Mathematical Models and Methods in Applied Sciences, 1(4), pp. 300-307 (2007)
• Cunningham, P., A Taxonomy of Similarity Mechanisms for Case-Based Reasoning, IEEE Trans. Knowledge and Data Engineering, Vol. 21 (11), pp. 1532-1543, (2009)
• Detyniecki, M., Mathematical aggregation operators and their application to video querying, Research Report, LIP6, Paris (2001)
• Dubois, D. and Prade, H., On the use of aggregation operations in information fusion processes, Fuzzy Sets and Systems, 142, pp. 143-161 (2004)
• Fred, A.N.L. and Jain, A.K., Learning Pairwise Similarity for Data Clustering, 18th International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 925-928 (2006)
• De Mantaras, R.L., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., et al., Retrieval, reuse, revision, and retention in case-based reasoning, Knowledge Engineering Review, 20(3), pp. 215-240 (2005)
• Jain, A K., Murty, M.N. and Flynn, P.J., Data clustering: a review,ACM Computing Surveys, 31(3), pp. 264-323 (1999)
• Jain, A., Data clustering: 50 years beyond k-means, Pattern Recognition Letters, 31(8), pp. 651-666 (2010)
• Nourizadeh, A., Blanchard, F., Aït Younes, A., Delemer, B. and Herbin, M., Data Analysis of Insulin Therapy in the Elderly Type 2 Diabetic Patients, Studia Informatica Universalis, 11(3), pp. 32-49 (2013)
• Novak, D., Batko, M. and Zezula, P., Large-scale similarity data management with distributed metric index, Information Processing and Management, 48(5), pp. 855-872 (2012)
• Perez, E.C. and Lamata, M.T., OWA weights determination by means of linear functions, Mathware and Soft Computing, 16, pp. 107-122 (2009)
• Yager, R., On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Systems, Man and Cybern., 18(1), pp. 183-190 (1988)
• Yager, R., Fuzzy logic methods in recommender systems, Fuzzy Sets and Systems, 136, pp. 133-149 (2003)
• Zenebe, A. and Norcio, A.F., Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems, Fuzzy Sets and Systems, 160, pp. 76-94 (2009)
• Zerzucha, P. and Walczak, B., Concept of (dis)similarity in data analysis, Trends in Analytical Chemistry, 38, pp. 116-128 (2012)
• Zwick, R., Caristein, E. and Badescu, D.V., Measures of similarity among fuzzy concepts: A comparative analysis, International Journal of Approximate Reasoning, 1, pp. 221-242 (1987)

Identyfikatory

DOI

10.15439/2015F174