Olfa Nasraoui

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This paper presents new algorithms (Fuzzy c-Medoids or FCMdd and Robust Fuzzy c-Medoids or RFCMdd) for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the(More)
In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a di1erent set of feature weights for each identi2ed cluster. The cluster dependent feature weights o1er two advantages. First, they guide(More)
In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner’s recent navigation history, as well as exploiting similarities and dissimilarities among learners’(More)
AbstructTraditionally, prototype-based fuzzy clustering algorithms such as the Fuzzy C Means (FCM) algorithm have been used to find “compact” or “filled” clusters. Recently, there have been attempts to generalize such algorithms to the case of hollow or “shell-like” clusters, i.e., clusters that lie in subspaces of feature space. The shell clustering(More)
The fuzzy c spherical shells (FCSS) algorithm is specially designed to search for clusters that can be described by circular arcs or, generally, by shells of hyperspheres. A new approach to the FCSS algorithm is presented. This algorithm is computationally and implementationally simpler than other clustering algorithms that have been suggested for this(More)
The proliferation of information on the World Wide Web has made the personalization of this information space a necessity. An important component of Web personalization is to mine typical user pro les from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classi cation or clustering methods seem(More)
Artificial Immune System (AIS) models hold many promises in the field of unsupervised learning. However, existing models are not scalable, which makes them of limited use in data mining. We propose a new AIS based clustering approach (TECNO-STREAMS) that addresses the weaknesses of current AIS models. Compared to existing AIS based techniques, our approach(More)
We present a new approach to anomaly detection based on the Unsupervised Niche Clustering (UNC). The UNC is a genetic niching technique for clustering that can handle noise, and is able to determine the number of clusters automatically. The UNC uses the normal samples for generating a profile of the normal space (clusters). Each cluster can later be(More)
The proliferation of information on the World-Wide Web has made the personalization of this information space a necessity. An important part of Web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In this paper, we define the notion of a “user session” and a new distance measure between two web(More)