The growing usage of embedded devices and sensors in our daily lives has been profoundly reshaping the way we interact with our environment and our peers. As more and more sensors will pervade our future cities, increasingly efficient infrastructures to collect, process, and store massive amounts of data streams from a wide variety of sources will be required. Despite the different application-specific features and hardware platforms, sensor network applications share a common goal: periodically sample and store data collected from different sensors in a common persistent memory. In this article we present a clustering approach for rapidly and efficiently computing the best sampling rate which minimizes the SSE (Sum of Square Errors) for each particular sensor in a network. In order to evaluate the efficiency of the proposed approach, we carried out experiments on real electric power consumption data streams produced by a 1-thousand sensor network provided by the French energy group-EDF (Electricite de France).

Various propositions to solve the problem of symbolic data clustering are available in the literature. This paper introduces a comparative study among some well known dissimilarity functions treating symbolic data. An extension of the fuzzy c-means clustering algorithm is used to create groups of individuals characterized by symbolic variables of mixed types. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion dependent on the dissimilarity function. Experiments involving benchmark data sets are carried out in order to compare the accuracy of each function.

Taking the temporal dimension into account during the analysis of Web usage data has become a necessity since the way a site is visited may well evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. Consequently, the models associated with these behaviors must be continuously updated. One solution to this problem is to update these models using summaries obtained by means of an evolutionary approach based on clustering methods. To do this, we carry out various clustering strategies that are applied on time sub-periods. We compare the results obtained using this method with those reached by traditional global analysis.

The analysis of a web site based on usage data is an important task as it provides insight into the organization of the site and its adequacy regarding user needs. This allows the relationship between prior categories and user browsing patterns to be explored. In this paper we propose an approach for discovering the profiles of visitor groups. To this end, we begin by mapping user interests into symbolic objects, which is the basis of the Symbolic Data Analysis and represents here a successful interaction of the user with the web site. We then identify groups of users with similar behavior by means of a dynamic clustering approach applying a context dependent dissimilarity measure. The method was applied to identify visitor groups of a web site in the educational domain and also to analyze the traces of different user behavior.