Clustering attempts to discover significant groups present in a data set. It is an unsupervised process. It is difficult to define when a clustering result is acceptable. Thus, several clustering validity indices are developed to evaluate the quality of clustering algorithms results. In this paper, we propose to improve the quality of a clustering algorithm called ”CLUSTER” by using a validity ...

Clustering is a central unsupervised learning task with a wide variety of applications. Not surprisingly, there exist many clustering algorithms. However, unlike classification tasks, in clustering, different algorithms may yield dramatically different outputs for the same input sets. A major challenge is to develop tools that may help select the more suitable algorithm for a given clustering t...

We introduce a distance measure based on the idea that two vectors are considered similar if they lead to similar predictive probability distributions. The suggested approach avoids the scaling problem inherent to many alternative techniques as the method automatically transforms the original attribute space to a probability space where all the numbers lie between 0 and 1. The method is also ex...

In this paper, we present an unsupervised grouping approach of data items (images) in the context of content-based exploration of large image databases. More particularly, we highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual ...

The class imbalance problem causes a classier to overt the data belonging to the class with the greatest number of training examples. The purpose of this paper is to argue that methods that equalize class membership are not as e ective as possible when applied blindly and that improvements can be obtained by adjusting for the within-class imbalance. A guided resampling technique is proposed and...

In order to investigate the local filtering behavior of the Retinex model, we propose a new implementation in which paths are replaced by 2-D pixel sprays, hence the name “random spray Retinex.” A peculiar feature of this implementation is the way its parameters can be controlled to perform spatial investiga-tion. The parameters’ tuning is accomplished by an unsupervised method based on quantit...

In this paper, we present a wavelet based approach which tries to automatically find the number of clusters present in a data set, along with their position and statistical properties. The only information supplied to the method is the data set to analyze and a confidence level parameter. Most of the usual methods for cluster analysis and unsupervised classification do not automatically determi...

We present a distillation algorithm which operates on a large, unstructured, and noisy collection of internet images returned from an online object query. We introduce the notion of a distilled set, which is a clean, coherent, and structured subset of inlier images. In addition, the object of interest is properly segmented out throughout the distilled set. Our approach is unsupervised, built on...

Clustering is an unsupervised learning task which provides a decomposition of a dataset into subgroups that summarize the initial base and give information about its structure. We propose to enrich this result by a numerical coefficient that describes the cluster representativity and indicates the extent to which they are characteristic of the whole dataset. It is defined for a specific cluster...