The partitioning of a time-series into internally homogeneous segments is an important data mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, th...

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of f...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

Aim of the paper is to propose a segmentation technique based on the Bagged Clustering (BC) method. In the partitioning step of the BC method, B bootstrap samples with replacement are generated by drawing from the original sample. The Fuzzy C–Medoids Clustering (FCMdC) method is run on each bootstrap sample, obtaining (B×C) medoids and the membership degrees of each unit to the different cluste...

This paper proposes a new decision making structure to determine the appropriate product delivery strategy for different products in a manufacturing system among make-to-stock, make-toorder, and hybrid strategy. Given product delivery strategies for all products in the manufacturing system, the position of the Order Penetrating Point (OPP) can be located regarding the delivery strategies among ...

Clustering is a basic task in a variety of machine learning applications. Partitioning a set of input vectors into compact, wellseparated subsets can be severely affected by the presence of modelincompatible inputs called outliers. The present paper develops robust clustering algorithms for jointly partitioning the data and identifying the outliers. The novel approach relies on translating scar...

Many forecasting models have been proposed to improve forecasting accuracy. Recently, Chen at al. model which incorporates three concepts of Fibonacci sequence, framework of Song and Chissom's model and weighted method of Yu's model has been proposed as a method to improve forecasting accuracy. However, the issue on lengths of intervals has not been investigated by Chen et al. despite Huarng ad...

High dimensional data clustering arises naturally in a lot of domains, and have regularly presented a great deal with for usual data mining techniques. In this paper, presents an optimal perspective on the problem of Consensus Clustering in high-dimensional data. The proposed method called ―Fuzzy based and kernel mappings with Consensus Neighboring clustering (FKCNC)‖, which takes as key measur...

The automatic clustering problem of a large and complex data set into different homogeneous clusters is a challenging task. The choose of a variable length clusters centers using a good method to generate the maskers is an important phase in the evolution of a global search heuristics algorithms. In this paper, a new technique of real-coded modified differential evolution based automatic fuzzy ...

The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessment of EDZ, based upon rock mass characteristics...