Feature selection has been extensively used in supervised learning, such as text classification. It (Devaney and Ram 1997) minimizes the high dimensionality of the feature space and also offers improved data understanding which enhances the clustering result. The chosen feature set should consist of adequate data about the original data set. It is believed that feature selection can enhance the...

Multiple features such as the margin, the shape and the texture of plant leaves are of great importance for classification of plant species, as they are often regarded as the unique features to identify plants. In this paper, we study the performance of a recently proposed semi-supervised fuzzy clustering algorithm with feature discrimination for leaf classification, based on features generated...

In many modern application ranges high-dimensional feature vectors are used to model complex real-world objects. Often these objects reside on different local sites. In this paper, we present a general approach for extracting knowledge out of distributed data sets without transmitting all data from the local clients to a server site. In order to keep the transmission cost low, we first determin...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Unions of subspaces provide a powerful generalization of single subspace models for collections of high-dimensional data; however, learning multiple subspaces from data is challenging due to the fact that segmentation—the identification of points that live in the same subspace—and subspace estimation must be performed simultaneously. Recently, sparse recovery methods were shown to provide a pro...

return maximization or risk minimization is goal in portfolio optimization based on mean variance theory. the structure of correlation matrices and individual variance of each asset are two main factors in optimization with risk minimization object. it’s necessary to use appropriate variance and correlation coefficient for time series with clustering volatilities feature, too. in this research,...

It is well-known that traditional clustering methods considering all dimensions of the feature space usually fail in terms of efficiency and effectivity when applied to high-dimensional data. This poor behavior is based on the fact that clusters may not be found in the high-dimensional feature space, although clusters exist in subspaces of the feature space. To overcome these limitations of tra...

Sequence clustering is one of most fundamental topics which can be applied in various research field. Most of previous work on sequence clustering is dedicated to the single-label clustering in which the whole similarity of equal-length sequence is considered and measured by Euclidean distance function. However, intrinsic properties behind sequence demand the multi-label clustering. In addition...

This paper presents computationally efficient object detection, matching and categorization via Agglomerative Correspondence Clustering (ACC). We implement ACC for feature correspondence and object-based image matching exploiting both photometric similarity and geometric consistency from local invariant features. Objectbased image matching is formulated here as an unsupervised multi-class clust...

Fuzzy c-means (FCM) is an important clustering algorithm. However, it does not consider the impact of different feature on clustering. In this paper, we present a fuzzy clustering algorithm with the generalized entropy of feature weights FCM (GEWFCM). By introducing feature weights and adding regularized term of their generalized entropy, a new objective function is proposed in terms of objecti...