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Fuzzy data mining: effect of fuzzy discretization
Proceedings 2001 IEEE International Conference on Data Mining, 2002When we generate association rules, continuous attributes have to be discretized into intervals while our knowledge representation is not always based on such discretization. For example, we usually use some linguistic terms (e.g., young, middle age, and old) for dividing our ages into some fuzzy categories.
Hisao Ishibuchi +2 more
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ON THE ROLE OF INTERPRETABILITY IN FUZZY DATA MINING
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2007Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users.
Corrado Mencar +2 more
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Fuzzy data mining for time-series data
Applied Soft Computing, 2012Time series analysis has always been an important and interesting research field due to its frequent appearance in different applications. In the past, many approaches based on regression, neural networks and other mathematical models were proposed to analyze the time series.
Chun-Hao Chen +2 more
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Fuzzy data mining query language
1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111), 2002Presents a fuzzy data mining query language, FDMQL. This query language supports fuzzy data mining queries and facilitates intelligent searching. FDMQL offers three functionalities: (1) retrieval of tuples which contain fuzzy attributes; this functionality of FDMQL can be thought of as a fuzzy SQL language; (2) an evaluator of fuzzy linguistic ...
S. A. Maelainin, A. Bensaid
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Data Mining of Gene Expression Data by Fuzzy and Hybrid Fuzzy Methods
IEEE Transactions on Information Technology in Biomedicine, 2010Microarray studies and gene expression analysis have received tremendous attention over the last few years and provide many promising avenues toward the understanding of fundamental questions in biology and medicine. Data mining of these vasts amount of data is crucial in gaining this understanding.
Gerald Schaefer, Tomoharu Nakashima
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Fuzzy Prototypes for Fuzzy Data Mining
2000This paper shows a summarization method. We focus on the construction of fuzzy prototypes by means of typical values. For this purpose, we use a formal framework of measures of comparison of values of attributes of data for the management of fuzzy databases especially for data mining.
Maria Rifqi, Sophie Monties
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Data Mining for Fuzzy Relational Data Servers
2007Methods of Perception-based Data Mining (Data Miners DM) for fuzzy relational data servers are considered in the article. The considering problems of DM are limited by problems of clustering and mining of dependences in the form of fuzzy rules because these problems are especially important in practice.
A. P. Velmisov +2 more
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Summary SQL — A fuzzy tool for data mining
Intelligent Data Analysis, 1997The increasing use of computers for transactions and communication have created mountains of data that contain potentially valuable knowledge. To search for this knowledge we have to develop a new generation of tools, which have the ability of flexible querying and intelligent searching.
Dan Rasmussen, Ronald R. Yager
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2002 Annual Meeting of the North American Fuzzy Information Processing Society Proceedings. NAFIPS-FLINT 2002 (Cat. No. 02TH8622), 2003
A fuzzy spatial data mining technique has been developed to extract relationships describing relative position of classes of objects from raster images. Several different rule forms are described which represent different types of directional relationships between classes of objects.
G.B. Smith, S.M. Bridges
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A fuzzy spatial data mining technique has been developed to extract relationships describing relative position of classes of objects from raster images. Several different rule forms are described which represent different types of directional relationships between classes of objects.
G.B. Smith, S.M. Bridges
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Optimized Fuzzy Classification for Data Mining
2004F uzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods.
Myung-Won Kim, Joung Woo Ryu
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