In this paper we will describe several methods that solve, using mathematical programming, different problems that, although can be used in others fields in the fuzzy modelling context, we have used them in the of fuzzy classification context. These problems are: feature selection, rule selection and clustering. In order to evaluate the accuracy of these methods, a lot of data sets have been us...

Finding repetitive subsequences in genome is a challengeable problem in bioinformatics research area. A lot of approaches have been proposed to solve the problem, which could be divided to library base and de novo methods. The library base methods use predetermined repetitive genome’s subsequences, where library-less methods attempt to discover repetitive subsequences by analytical approach...

There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric models [3], MLP [4]). The main motivation of these methods is to benefit from the enforced inner structure of the data. This paper presents how functional data can be used with RBFN, and how the inner structure of the form...

AbsfracfData mining has a lot of e-Commerce applications. The key problem is how to tind useful hidden patterns for better business applications. For these problems, granular fuzzy Web intelligence techniques are used to implement the granular fuzzy Web data mining system for available historical data of the credit company customers. Fuzzy computing and granular computing are used to design the...

For the peak picking of tempo candidates, applying kmeans clustering on tempo curve is straightforward and leading to good result. But the tempo candidates obtained from tempo curve are limited and lose a lot of information for possible tempi. The study proposes the local maximum peak picking method to increase the number and information of possible tempo candidates. Therefore, the accuracy of ...

Instead of relying on setting an arbitrary threshold current value as in traditional IDDQ testing, clustering based test technique relies on the characteristics of an IC with respect to all the other ICs in a lot to make a test decision. An improvement in the cluster analysis technique for IDDQ testing is presented. Results of applying this technique to data collected on a high volume graphics ...

There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric models [3], MLP [4]). The main motivation of these methods is to benefit from the enforced inner structure of the data. This paper presents how functional data can be used with RBFN, and how the inner structure of the form...

In the previous study, we have proposed the Community Self-Organizing Map (CSOM) that the neurons create some neuron-community according to their winning frequencies. In this study, we apply CSOM to clustering and data extraction for various input data including a lot of noises, and we investigate its numerical efficiency by using correct answer rate. We confirm that CSOM creates some communiti...