We propose two algorithms for robust two-mode partitioning of a data matrix in the presence of outliers. First we extend the robust k-means procedure to the case of biclustering, then we slightly relax the definition of outlier and propose a more flexible and parsimonious strategy, which anyway is inherently less robust. We discuss the breakdown properties of the algorithms, and illustrate the ...

Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning. Cluster analysis is a formal study of methods for understanding and algorithm for learning. K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms. When there is no prior knowledge about the distribution of data sets, K-mean is the first choice fo...

The ability to monitor the progress of students’ academic performance is a critical issue to the academic community of higher learning. A system for analyzing students’ results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k-mean clustering algorithm for...

The algorithm used by the authors in the bird identification task of LifeCLEF 2016 consists in creating a dictionary of MFCC-based words using k-means clustering, computing histograms of these words over short audio segments and feeding them to a random forest classifier. The official score achieved is 0.15 MAP.

Grid computing has been identified as an instrument to fulfil high computational demand, a promising approach for higher resource utilization, and an instrument for cost reduction. The full potential of cost savings can be tapped when incentives are set such that demand is shifted to periods or hardware with lower demand, thereby flattening the demand. To set such incentives, it is mandatory to...

Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms se...

This paper deals with the application of Data Mining in the education sector. Generally the benefits of Data Mining are taken in the commercial fields. The study given, proposed a quiet different field where we can use the Data Mining and enhance the quality of education. In the given paper the performance of an institute students were studied. The study takes the performance of students in the...

This letter presents K-P-Means, a novel approach for hyperspectral endmember estimation. Spectral unmixing is formulated as a clustering problem, with the goal of K-P-Means to obtain a set of “purified” hyperspectral pixels to estimate endmembers. The K-P-Means algorithm alternates iteratively between two main steps (abundance estimation and endmember update) until convergence to yield final en...

A basic belief assignment can have up to 2 focal elements, and combining them with a simple conjunctive operator will need O(2) operations. This article proposes some techniques to limit the size of the focal sets of the bbas to be combined while preserving a large part of the information they carry. The first section revisits some well-known definitions with an algorithmic point of vue. The se...

We present a study of the clustering properties of medical publications for the aim of Evidence Based Medicine summarisation. Given a dataset of documents that have been manually assigned to groups related to clinical answers, we apply K-Means clustering and verify that the documents can be clustered reasonably well. We advance the implications of such clustering for natural language processing...