This paper presents an iterative Content Based Image Retrival(CBIR) system with Relevance Feedback (RF), in which M-band wavelet features are used as representation of images. The pixels are clustered using Fuzzy C-Means (FCM) clustering algorithm to obtain an image signature and Earth Mover’s Distance (EMD) is used as a distance measure. Fuzzy entropy based feature evaluation mechanism is used...

Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluste...

This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy eliminates the complication of other methods. Shannon entropy calculates the ratio of an element between faces, and fuzzy theory calculates the membership of the entropy with 1. More details will be mentioned in Section 3. The learning performanc...

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsu...

—Let f be an interval-valued fuzzy subset of a nite set U of size n. This yields n closed intervals inside the unit interval. Picking a point in each interval and dividing by the sum of the points gives rise to a probability density on the set of intervals. For a given measure of dispersion, such as entropy, the problem is to pick points that maximize (or minimize) this dispersion. The particul...

In this study, feature selection in classi cation based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classi er. ...

Abstract Feature selection based on the fuzzy neighborhood rough set model (FNRS) is highly popular in data mining. However, dependent function of FNRS only considers information present lower approximation decision while ignoring upper decision. This construction method may lead to loss some information. To solve this problem, paper proposes a joint entropy self-information measure (FNSIJE) an...

Utilizing renewable energies is identified as one of significant issues for economical and social significance in future human life. Thus, choosing the best renewable energy among renewable energy candidates is more important. To address the issue, multi-criteria group decision making (MCGDM) methods with imprecise information could be employed to solve these problems. The aim of this paper is ...

This article explains how to apply the deterministic annealing (DA) and simulated annealing (SA) methods to fuzzy entropy based fuzzy c-means clustering. By regularizing the fuzzy c-means method with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function, well known in statistical mechanics, is obtained, and, while optimizing its parameters by SA, the minimum of t...