Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifying and grouping those documents talking about the same event. Document clustering involves dividing a set of documents into non-overlapping clusters. In this paper, we present two document clustering algorithms: groupi...

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

The k-means algorithm is one of the most widely used methods to partition a dataset into groups of patterns. However, most k-means methods require expensive distance calculations of centroids to achieve convergence. In this paper, we present an efficient algorithm to implement a k-means clustering that produces clusters comparable to slower methods. In our algorithm, we partition the original d...

The paper describes variations of the classical k-means clustering algorithm that can be used effectively to address the so called Web-site Boundary Detection (WBD) problem. The suggested advantages offered by these techniques are that they can quickly identify most of the pages belonging to a web-site; and, in the long run, return a solution of comparable (if not better) accuracy than other cl...

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...