Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm easily affected by initial centers. Currently, scholars often use multiple population genetic (MPGA) to optimize centers, MPGA has insufficient global search ability and lacks self-adaptability, prone premature convergence, poor Therefore, paper proposes an adaptive FCM DMGA-FCM based on derivative...

Most of the clustering methods used in the clustering of chemical structures such as Ward’s, Group Average, Kmeans and Jarvis-Patrick, are known as hard or crisp as they partition a dataset into strictly disjoint subsets; and thus are not suitable for the clustering of chemical structures exhibiting more than one activity. Although, fuzzy clustering algorithms such as fuzzy cmeans provides an i...

This paper proposes a fuzzy clustering model for fuzzy data with outliers. The model is based on Wasserstein distance between interval valued data, which is generalized to fuzzy data. In addition, Keller’s approach is used to identify outliers and reduce their influences. The authors also define a transformation to change the distance to the Euclidean distance. With the help of this approach, t...

Medical image segmentation is an important tool in viewing and analyzing Magnetic Resonance Images (MRI) and solving variousranges of problems in medical imaging. This paper focuses the new approach to segmentation by clustering the image by Genetic Algorithm based Fuzzy C-means clustering (FCM). First segmentation can be done with the help of FCM. Fuzzy C-means can be used to segment the image...

this paper investigates the problem of just-in-time permutation flow shop scheduling with limited buffers and linear job deterioration in an uncertain environment. the fuzzy set theory is applied to describe this situation. a novel mixed-integer nonlinear program is presented to minimize the weighted sum of fuzzy earliness and tardiness penalties. due to the computational complexities, the prop...

This paper analyses a multi-compressor system for its performance failures and subsequent improvements. The logbook data of this system has been obtained. Data has been classified using various state-of-art data classification techniques. This paper presents a comparative analysis of Fuzzy clustering algorithm, Hard-c-means clustering and Gustafson-Kessel clustering algorithm. Data clustering e...

Time course measurements are becoming a common type of experiment in the use of microrarrays. The temporal order of the data and the varying length of sampling intervals are important and should be considered in clustering time-series. However, the shortness of gene expression time-series data limits the use of conventional statistical models and techniques for time-series analysis. To address ...

Fuzzy C-mean (FCM) is the most well-known and widely-used fuzzy clustering algorithm. However, one of the weaknesses of the FCM is the way it assigns membership degrees to data which is based on the distance to the cluster centers. Unfortunately, the membership degrees are determined without considering the shape and density of the clusters. In this paper, we propose an algorithm which takes th...

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an important problem in a wide variety of fields. Including data mining, pattern recognition, and bioinformatics. It aims to organize a collection of data items into...