Olfa Nasraoui

Low-complexity fuzzy relational clustering algorithms for Web mining

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (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 well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis.

Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II

Shell clustering algorithms are ideally suited for computer vision tasks such as boundary detection and surface approximation, particularly when the boundaries have jagged or scattered edges and when the range data is sparse. This is because shell clustering is insensitive to local aberrations, it can be performed directly in image space, and unlike traditional approaches it does assume dense data and does not use additional features such as curvatures and surface normals. The shell clustering algorithms introduced in Part I of this paper assume that the number of clusters is known, however, which is not the case in many boundary detection and surface approximation applications. This problem can be overcome by considering cluster validity. We introduce a validity measure called surface density which is explicitly meant for the type of applications considered in this paper, we show through theoretical derivations that surface density is relatively invariant to size and partiality (incompleteness) of the clusters. We describe unsupervised clustering algorithms that use the surface density measure and other measures to determine the optimum number of shell clusters automatically, and illustrate the application of the proposed algorithms to boundary detection in the case of intensity images and to surface approximation in the case of range images. >

Unsupervised learning of prototypes and attribute weights

Abstract 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 different set of feature weights for each identified cluster. The cluster dependent feature weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a learning system to improve its learning behavior. An extension of the algorithm to deal with an unknown number of clusters is also proposed. The extension is based on competitive agglomeration, whereby the number of clusters is over-specified, and adjacent clusters are allowed to compete for data points in a manner that causes clusters which lose in the competition to gradually become depleted and vanish. We illustrate the performance of the proposed approach by using it to segment color images, and to build a nearest prototype classifier.

Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies.

The fuzzy c spherical shells algorithm: A new approach

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 purpose. An unsupervised algorithm which automatically finds the optimum number of clusters is not known. It uses a cluster validity measure to identify good clusters, merges all compatible clusters, and eliminates spurious clusters to achieve the final results. Experimental results on several data sets are presented.

Advances in Web Mining and Web Usage Analysis

Adaptive Website Design Using Caching Algorithms.- Incorporating Usage Information into Average-Clicks Algorithm.- Nearest-Biclusters Collaborative Filtering with Constant Values.- Fast Categorization of Web Documents Represented by Graphs.- Leveraging Structural Knowledge for Hierarchically-Informed Keyword Weight Propagation in the Web.- How to Define Searching Sessions on Web Search Engines.- Incorporating Concept Hierarchies into Usage Mining Based Recommendations.- A Random-Walk Based Scoring Algorithm Applied to Recommender Engines.- Towards a Scalable kNN CF Algorithm: Exploring Effective Applications of Clustering.- Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach.- Predicting the Political Sentiment of Web Log Posts Using Supervised Machine Learning Techniques Coupled with Feature Selection.- Analysis of Web Search Engine Query Session and Clicked Documents.- Understanding Content Reuse on the Web: Static and Dynamic Analyses.

Web Usage Mining

With the continued growth and proliferation of e-commerce, Web services, and Web-based information systems, the volumes of clickstream, transaction data, and user profile data collected by Web-based organizations in their daily operations has reached astronomical proportions. Analyzing such data can help these organizations determine the life-time value of clients, design cross-marketing strategies across products and services, evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, provide more personalized content to visitors, and find the most effective logical structure for their Web space. This type of analysis involves the automatic discovery of meaningful patterns and relationships from a large collection of primarily semi-structured data, often stored in Web and applications server access logs, as well as in related operational data sources.

TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model

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 exhibits superior learning abilities, while at the same time, requiring low memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to other approaches is expected to be its ease of adaptation to the dynamic environment that characterizes several applications, particularly in mining data streams. We illustrate the ability of the proposed approach in detecting clusters in noisy data sets, and in mining evolving user profiles from Web clickstream data in a single pass. TECNO-STREAMS adheres to all the requirements of clustering data streams: compactness of representation, fast incremental processing of new data points, and clear and fast identification of outliers.

Multimodal representation, indexing, automated annotation and retrieval of image collections via non-negative matrix factorization

Massive image collections are increasingly available on the Web. These collections often incorporate complementary non-visual data such as text descriptions, comments, user ratings and tags. These additional data modalities may provide a semantic complement to the image visual content, which could improve the performance of different image content analysis tasks. This paper presents a novel method based on non-negative matrix factorization to generate multimodal image representations that integrate visual features and text information. The proposed approach discovers a set of latent factors that correlate multimodal data in the same representation space. We evaluated the potential of this multimodal image representation in various tasks associated to image indexing and search. Experimental results show that the proposed method highly outperforms the response of the system in both tasks, when compared to multimodal latent semantic spaces generated by a singular value decomposition.

The Fuzzy C Quadratic Shell clustering algorithm and the detection of second-degree curves

Abstract This paper introduces a new fuzzy clustering algorithm called the Fuzzy C Quadric Shells algorithm which is expressly designed to seek clusters that can be described by segments of second-degree curves, or more generally by segments of shells of hyperquadrics.