Mohamed Nadif

A dynamic collaborative filtering system via a weighted clustering approach

A collaborative filtering system (CF) aims at filtering huge amount of information, in order to guide users of web applications towards items that might interest them. Such a system, consists in recommending a set of personalized items for an active user, according to the preferences of other similar users. Existing methods, such as memory and Matrix Factorization (MF) approaches can achieve very good recommendation accuracy, unfortunately they are computationally very expensive. Applying such approaches to real-world applications in which users, items and ratings are frequently updated remains therefore a challenge. To address this problem, we propose a novel efficient incremental CF system, based on a weighted clustering approach. Our system is designed to provide a high quality of recommendations with a very low computation cost. In contrast to existing incremental methods, the complexity of our approach does not depend on the number of users and items. Our CF system is therefore suitable for dynamic settings, involving huge databases, in which available information evolves rapidly (i.e, submission of new ratings, update of existing ratings, appearance of new users and new items). Numerical experiments, conducted on several real-world datasets, confirm the efficiency and the effectiveness of our method, by demonstrating that it is significantly better than existing incremental CF methods in terms of both scalability and recommendation quality.

A Semi-NMF-PCA Unified Framework for Data Clustering

In this work, we propose a novel way to consider the clustering and the reduction of the dimension simultaneously. Indeed, our approach takes advantage of the mutual reinforcement between data reduction and clustering tasks. The use of a low-dimensional representation can be of help in providing simpler and more interpretable solutions. We show that by doing so, our model is able to better approximate the relaxed continuous dimension reduction solution by the true discrete clustering solution. Experiment results show that our method gives better results in terms of clustering than the state-of-the-art algorithms devoted to similar tasks for data sets with different proprieties.

Mutual information, phi-squared and model-based co-clustering for contingency tables

Many of the datasets encountered in statistics are two-dimensional in nature and can be represented by a matrix. Classical clustering procedures seek to construct separately an optimal partition of rows or, sometimes, of columns. In contrast, co-clustering methods cluster the rows and the columns simultaneously and organize the data into homogeneous blocks (after suitable permutations). Methods of this kind have practical importance in a wide variety of applications such as document clustering, where data are typically organized in two-way contingency tables. Our goal is to offer coherent frameworks for understanding some existing criteria and algorithms for co-clustering contingency tables, and to propose new ones. We look at two different frameworks for the problem of co-clustering. The first involves minimizing an objective function based on measures of association and in particular on phi-squared and mutual information. The second uses a model-based co-clustering approach, and we consider two models: the block model and the latent block model. We establish connections between different approaches, criteria and algorithms, and we highlight a number of implicit assumptions in some commonly used algorithms. Our contribution is illustrated by numerical experiments on simulated and real-case datasets that show the relevance of the presented methods in the document clustering field.

Hard and fuzzy diagonal co-clustering for document-term partitioning

We propose a hard and a fuzzy diagonal co-clustering algorithms built upon the double K-means to address the problem of document-term co-clustering. At each iteration, the proposed algorithms seek a diagonal block structure of the data by minimizing a criterion based on both the variance within the class and the centroid effect. In addition to be easy-to-interpret and effective on sparse binary and continuous data, the proposed algorithms, Hard Diagonal Double K-means (DDKM) and Fuzzy Diagonal Double K-means (F-DDKM), are also faster than other state-of-the-art clustering algorithms. We evaluate our contribution using synthetic data sets, and real data sets commonly used in document clustering.

Sparse Poisson Latent Block Model for Document Clustering

Over the last decades, several studies have demonstrated the importance of co-clustering to simultaneously produce groups of objects and features. Even to obtain object clusters only, using co-clustering is often more effective than one-way clustering, especially when considering sparse high dimensional data. In this paper, we present a novel generative mixture model for co-clustering such data. This model, the Sparse Poisson Latent Block Model (SPLBM), is based on the Poisson distribution, which arises naturally for contingency tables, such as document-term matrices. The advantages of SPLBM are two-fold. First, it is a rigorous statistical model which is also very parsimonious. Second, it has been designed from the ground up to deal with data sparsity problems. As a consequence, in addition to seeking homogeneous blocks, as other available algorithms, it also filters out homogeneous but noisy ones due to the sparsity of the data. Experiments on various datasets of different size and structure show that an algorithm based on SPLBM clearly outperforms state-of-the-art algorithms. Most notably, the SPLBM-based algorithm presented here succeeds in retrieving the natural cluster structure of difficult, unbalanced datasets which other known algorithms are unable to handle effectively.

Simultaneous Semi-NMF and PCA for Clustering

Cluster analysis is often carried out in combination with dimension reduction. The Semi-Non-negative Matrix Factorization (Semi-NMF) that learns a low-dimensional representation of a data set lends itself to a clustering interpretation. In this work we propose a novel approach to finding an optimal subspace of multi-dimensional variables for identifying a partition of the set of objects. The use of a low-dimensional representation can be of help in providing simpler and more interpretable solutions. We show that by doing so, our model is able to learn low-dimensional representations that are better suited for clustering, outperforming not only Semi-NMF, but also other NMF variants.

Exploratory Analysis of Text Collections Through Visualization and Hybrid Biclustering

We propose a visual analytics tool to support analytic journalists in the exploration of large text corpora. Our tool combines graph modularity-based diagonal biclustering to extract high-level topics with overlapping bi-clustering to elicit fine-grained topic variants. A hybrid topic treemap visualization gives the analyst an overview of all topics. Coordinated sunburst and heatmap visualizations let the analyst inspect and compare topic variants and access document content on demand.

Data Leak Aware Crowdsourcing in Social Network

Harnessing human computation for solving complex problems call spawns the issue of finding the unknown competitive group of solvers. In this paper, we propose an approach called Friendlysourcing to build up teams from social network answering a business call, all the while avoiding partial solution disclosure to competitive groups. The contributions of this paper include (i) a clustering based approach for discovering collaborative and competitive team in social network (ii) a Markov-chain based algorithm for discovering implicit interactions in the social network.

Multi-manifold matrix decomposition for data co-clustering

We propose a novel Multi-Manifold Matrix Decomposition for Co-clustering (M3DC) algorithm that considers the geometric structures of both the sample manifold and the feature manifold simultaneously. Specifically, multiple candidate manifolds are constructed separately to take local invariance into account. Then, we employ multi-manifold learning to approximate the optimal intrinsic manifold, which better reflects the local geometrical structure, by linearly combining these candidate manifolds. In M3DC, the candidate manifolds are obtained using various manifold-based dimensionality reduction methods. These methods are based on different rationales and use different metrics for data distances. Experimental results on several real data sets demonstrate the effectiveness of our proposed M3DC. HighlightsWe consider the geometric structures of both sample and feature manifolds.To reduces the complexity, we use two low-dimensional intermediate matrices.We employ multi-manifold learning to approximate the intrinsic manifold.The intrinsic manifold is constructed by linearly combining multiple manifolds.The candidate manifolds are constructed using six dimensionality reduction methods.

Block Mixture Model for the Biclustering of Microarray Data

An attractive way to make biclustering of genes and conditions is to adopt a Block Mixture Model (BMM). Approaches based on a BMM operate thanks to a Block Expectation Maximization (BEM) algorithm and/or a Block Classification Expectation Maximization (BCEM) one. The drawback of these approaches is their difficulty to choose a good strategy of initialization of the BEM and BCEM algorithms. This paper introduces existing biclustering approaches adopting a BMM and suggests a new fuzzy biclustering one. Our approach enables to choose a good strategy of initialization of the BEM and BCEM algorithms.