Recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of interand intra-cluster similarity, are very effective in producing hard clustering solutions for document datasets and outperform traditional partitional and agglomerative algorithms. In this paper we study the extent to which these criterion funct...

In text analytics (Srivastava & Sahami 2009), document clustering refers to the problem of automatically grouping documents into different groups (known as clusters), such that documents in one cluster are similar to each other while being dissimilar from the ones in a different cluster. Typically, the dataset is represented using the vector model in which a set of m documents with n unique wor...

form of medical reports. Such documents contain important information about patients, disease progression and management, but are difficult to analyse with conventional data mining techniques due to their unstructured nature. Clustering the medical documents into small number of meaningful clusters may facilitate discovering patterns by allowing us to extract a number of relevant features from ...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets....

This paper presents a new segmentation strategy, based on a blended procedure whose goal is to combine several segmentation maps in order to finally get a more reliable and accurate segmentation result. The fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associa...

In this paper we fully describe a novel clustering method for malware, from the transformation of data into a manipulable standardised data matrix, finding the number of clusters until the clustering itself including visualisation of the high-dimensional data. Our clustering method deals well with categorical data and clusters the behavioural data of 17,000 websites, acquired with Capture-HPC, ...

Keywords: The k-modes algorithm Initialization method Initial cluster centers Density Distance a b s t r a c t The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers...

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. Standard partitional or agglomerative clustering methods efficiently compute results to this end. However, the bag of words representation used for these clustering methods is often unsatisfactory as it ...

In recent years, many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Surprisingly, very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms for numerical optimization, which are hardly known outside the heuristic...