Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...

Most clustering algorithms produce a single clustering solution. Similarly, feature selection for clustering tries to find one feature subset where one interesting clustering solution resides. However, a single data set may be multi-faceted and can be grouped and interpreted in many different ways, especially for high dimensional data, where feature selection is typically needed. Moreover, diff...

This paper presents a novel approach to feature extraction for face recognition. This approach extends a previously developed method that incorporated the feature extraction techniques of GEFE ML (Genetic and Evolutionary Feature Extraction – Machine Learning) and Darwinian Feature Extraction). The feature extractors evolved by GEFE ML are superior to traditional feature extraction methods in t...

The Author Identification task for PAN 2016 consisted of three different Sub-tasks: authorship clustering, authorship links and author diarization. We developed a machine learning approaches for two of three of these tasks. For the two authorship related tasks we created various sets of feature spaces. The challenge was to combine these feature spaces to enable the machine learning algorithms t...

Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better t...

Due to the flourish of World Wide Web and the rapid development of the Internet technology, the increasing volume of digital textual data become more and more unmanageable, therefore the importance of text classification has gained significant attention. Text classification pose some specific challenges such as high dimensionality with each document (data point) having only a very small subset ...

The problem of inferring a clustering of a data set has been the subject of much research in Bayesian analysis, and there currently exists a solid mathematical foundation for Bayesian approaches to clustering. In particular, the class of probability distributions over partitions of a data set has been characterized in a number of ways, including via exchangeable partition probability functions ...

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

We propose a clustering algorithm that effectively utilizes feature order preferences, which have the form that feature s is more important than feature t. Our clustering formulation aims to incorporate feature order preferences into prototype-based clustering. The derived algorithm automatically learns distortion measures parameterized by feature weights which will respect the feature order pr...

Text classification is a challenging task due to the large dimensionality of the feature vector. To alleviate this problem, feature reduction techniques are applied for reducing the amount of time and complexity for text classification. In this paper, we propose a novel fuzzy self constructing algorithm for feature clustering. Feature clustering is a feature reduction method which drastically r...