permeability can be directly measured using cores taken from the reservoir in the laboratory. due to high cost associated with coring, cores are available in a limited number of wells in a field. many empirical models, statistical methods, and intelligent techniques were suggested to predict permeability in un-cored wells from easy-to-obtain and frequent data such as wireline logs. the main obj...

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets t...

MOTIVATION It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. In order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS We apply Fuzzy ART as a clustering method for analyzing the time series expression...

Allowing the similarity measure to be negative, this paper generalizes the clustering model to include not only the traditional hard and fuzzy clustering but also a new semi-fuzzy clustering. Then the robust semi-fuzzy clustering is introduced and used for brain MR image segmentation.

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...

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...