Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common propertyoften proximity according to some defined distance measure. Among various types of clustering techniques, K-Means, Hierarchical and Make Density Based clustering are the most popular algorithms. Clustering Techniques are very usefu...

In these days WEKA has become one of the most important data mining and machine learning tools. Despite the fact that it incorporates many algorithms, on the classification area there are still some unimplemented features. In this paper we cover some of the missing features that may be useful to researchers and developers when working with decision tree classifiers. The rest of the paper presen...

An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in s...

Özet. Günümüzde pek çok kurum mevcut verilerini ilişkisel veri tabanlarında saklamakta ve modellemelerini bu verileri kullanarak gerçekleştirmektedir. Kurumsal veri modellerinin karmaşıklığı, veriye ait özelliklerin çokluğu ve veri miktarının fazlalığı, veri üzerinde her türlü analizin (kümeleme, regresyon, vb.) yapılmasını zorlaştırmaktadır. Bu nedenle veri kümeleri üzerinde tahmin gücü yüksek...

In these days WEKA has become one of the most important data mining and machine learning tools. Despite the fact that it incorporates many algorithms, on the classification area there are still some unimplemented features. In this paper we cover some of the missing features that may be useful to researchers and developers when working with decision tree classifiers. The rest of the paper presen...

This paper presents the implementation on a healthcare dataset using data mining tools to find important parameters that reflect the effect of diabetes on kidney of patients. This is done with the use of Kidney Function Tests (KFT). The data mining tools used are Tanagra and Weka with the application of C4.5 Algorithm which is based on decision trees. This paper compares the result given by Wek...

WEKA is a workbench for machine learning that is intended to aid in the application of machine learning techniques to a variety of real-world problems, in particular, those arising from agricultural and horticultural domains. Unlike other machine learning projects, the emphasis is on providing a working environment for the domain specialist rather than the machine learning expert. Lessons learn...