This study focused on development and application of efficient algorithm for clustering and classification of supervised visual data. In machine learning clustering classification and clustering is most useful techniques in pattern recognition and computer vision. Existing techniques for subspace clustering makes use of rank minimization and spars based that are computationally expensive and ma...

We study a supervised clustering problem seeking to cluster either features, tasks or sample points using losses extracted from supervised learning problems. We formulate a unified optimization problem handling these three settings and derive algorithms whose core iteration complexity is concentrated in a k-means clustering step, which can be approximated efficiently. We test our methods on bot...

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn th...

We study a supervised clustering problem seeking to cluster either features, tasks or sample points using losses extracted from supervised learning problems. We formulate a unified optimization problem handling these three settings and derive algorithms whose core iteration complexity is concentrated in a k-means clustering step, which can be approximated efficiently. We test our methods on bot...

Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization clustering algorithm based on squared-loss mutual information to effectively ...

This paper presents Clustering Based Document classification and analysis of data. The proposed Clustering Based classification and analysis of data approach is based on Unsupervised and Supervised Document Classification. In this paper Unsupervised Document and Supervised Document Classification are used. In this approach Document collection, Text Preprocessing, Feature Selection, Indexing, Cl...

This paper elaborates the approach used by the Applied Data Mining Research Group (ADMRG) for the Social Event Detection (SED) Tasks of the 2013 MediaEval Benchmark. We participated in the semi-supervised clustering task as well as the classification of social events task. The constrained clustering algorithm is utilized in the semi-supervised clustering task. Several machine learning classifie...

A new framework for adapting common ensemble clustering 9 methods to solve the image segmentation combination problem is pre10 sented. The framework is applied to the parameter selection problem in 11 image segmentation and compared with supervised parameter learning. 12 We quantitatively evaluate 9 ensemble clustering methods requiring a 13 known number of clusters and 4 with adaptive estimati...

Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup, and show how these classification tasks can usefully be analyzed as weakly supervised clustering problems. We propose three approaches to solving the weakly supervised clustering problem, including a...

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameter...