In data mining clustering techniques are used to group together the objects showing similar characteristics within the same cluster and the objects demonstrating different characteristics are grouped into clusters. Clustering approaches can be classified into two categories namelyHard clustering and Soft clustering. In hard clustering data is divided into clusters in such a way that each data i...

In this paper, we propose a new method to specify the sequence of parameter values for a fuzzy clustering algorithm by using Q-learning. In the clustering algorithm, we employ similarities between two data points and distances from data to cluster centers as the fuzzy clustering criteria. The fuzzy clustering is achieved by optimizing an objective function which is solved by the Picard iteratio...

Kernelized Fuzzy C-Means clustering technique is an attempt to improve the performance of the conventional Fuzzy C-Means clustering technique. Recently this technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like th...

This paper explores the topic of fuzzy clustering, feature selection, and membership function optimization. Feature selection plays a crucial role for all fuzzy clustering applications, as the selection of appropriate features determines the quality of the resulting clusters. We will show how fuzzy clustering can be applied to data mining problems by introducing some of the most commonly used c...

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valu...

How to carry out the stakeholders managements effectively, one major issue is to continuously meet the demands of the stakeholders. The purpose of this paper is to clarify the disorganized information of stakeholders demands and correctly deal with it. The paper has expounded on the project stakeholders' demands information, such as fuzziness, randomness, dynamics, diversity and contradictorine...

In semisupervised fuzzy clustering, this article extends the traditional pairwise constraint (i.e., must-link or cannot-link) to constraint. The allows a supervisor provide grade of similarity dissimilarity between implicit vectors pair samples. This can represent more complicated relationship samples and avoid eliminating characteristics. Then, we propose clustering with constraints (SSFPC). n...

This work proposes how to generate a set of fuzzy rules from a data set using a clustering algorithm, the GKPFCM. If we recommend a number of clusters, the GKPFCM identifies the location and the approximate shape of each cluster. These ones describe the relations among the variables of the data set, and they can be expressed as conditional rules such as "If/Then". The GKPFCM provides membership...