Nicoleta Rogovschi

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.

BeSOM : Bernoulli on Self-Organizing Map

This paper introduces a probabilistic self-organizing map for clustering, analysis and visualization of multivariate binary data. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, BeSOM, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with two data sets taken from a public data set repository: a handwritten digit data set and a zoo data set. The results show a good quality of the topological ordering and homogenous clustering.

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

Probabilistic Mixed Topological Map for Categorical and Continuous Data

This paper introduces a new probabilistic topological map as generative model that includes mixture of Gaussian and Bernoulli distribution. This model is dedicated to cluster mixed data with continuous and categorical variables. This model is fitted by maximum likelihood using the EM algorithm. Examples using real data set allow to validate our model. The proposed approach has the advantage comparing to existing topological map of providing a set of prototype with the same coding as the learning data. More information is produced with this model that could be used in practical applications.

t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom

One of the dimension reduction (DR) methods for data-visualization, t-distributed stochastic neighbor embedding (t-SNE), has drawn increasing attention. t-SNE gives us better visualization than conventional DR methods, by relieving so-called crowding problem. The crowding problem is one of the curses of dimensionality, which is caused by discrepancy between high and low dimensional spaces. However, in t-SNE, it is assumed that the strength of the discrepancy is the same for all samples in all datasets regardless of ununiformity of distributions or the difference in dimensions, and this assumption sometimes ruins visualization. Here we propose a new DR method inhomogeneous t-SNE, in which the strength is estimated for each point and dataset. Experimental results show that such pointwise estimation is important for reasonable visualization and that the proposed method achieves better visualization than the original t-SNE.

A topographical nonnegative matrix factorization algorithm

We explore in this paper a novel topological organization algorithm for data clustering and visualization named TPNMF. It leads to a clustering of the data, as well as the projection of the clusters on a two-dimensional grid while preserving the topological order of the initial data. The proposed algorithm is based on a NMF (Nonnegative Matrix Factorization) formalism using a neighborhood function which take into account the topological order of the data. TPNMF was validated on variant real datasets and the experimental results show a good quality of the topological ordering and homogenous clustering.

A Topological k-Anonymity Model Based on Collaborative Multi-view Clustering

Data anonymization is the process of de-identifying sensitive data while preserving its format and data type. The masked data can be a realistic or a random sequence of data, dependent on the technique used for anonymization. Individual privacy can be at risk if a published data set is not properly de-identified. The most known approach of anonymization is k-anonymity that can be viewed as clustering with a constraint of k minimum objects in every cluster. In this paper, we propose a new anonymization approach based on multi-view topological collaborative clustering. The proposed method has the advantage of detecting the k level automatically. The aim of collaborative clustering is to reveal the common structure of data using different views on variables, it allows to take into account other knowledges without recourse to the data in an unsupervised learning frame. The proposed approach has been validated on several data sets, and experimental results have shown very promising performance.

t-Distributed stochastic neighbor embedding spectral clustering

This paper introduces a new topological clustering approach to cluster high dimensional datasets based on t-SNE (Stochastic Neighbor Embedding) dimensionality reduction method and spectral clustering. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [1] which are computational expensive and is not easy to apply on large-scale data sets. One of the issue of this problem is to reduce the dimensionality befor to cluster the dataset. The t-SNE method which performs good results for visulaization allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. Using t-SNE during the learning process will allow to reduce the dimensionality and to preserve the topology of the dataset by increasing the clustering accuracy. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.

Automated topological co-clustering using fuzzy features partition.

In this paper we introduce a new learning approach, which provides automated topological co-clustering based on Self-Organizing Map. The proposed approach (wd-TCoC) is computationally simple, learns a different features weights vector for each prototype (relevance vector) and estimate the data density distribution on the map to produce an automatic clustering. The features weights are computed for each observation during the learning process in order to attempt a clustering of both features and observations simultaneously. After the learning phase, a selection method with weight vectors is used to characterize features for each cluster. Compared to classical co-clustering algorithm, the features clustering in the proposed method is a fuzzy clustering, which allows a feature to belongs to different clusters and in this way to be able to eliminate the noisy features. The originality of the proposed method consists of 2 points: i) the co-clustering is made automatically, i.e. there is no parameter to fix to cluster the data and the features and ii) the features are fuzzy clustered. We illustrate the performance of the proposed approach using different data sets.

Spectral Clustering Trough Topological Learning for Large Datasets

This paper introduces a new approach for clustering large datasets based on spectral clustering and topological unsupervised learning. Spectral clustering method needs to construct an adjacency matrix and calculate the eigen-decomposition of the corresponding Laplacian matrix [4] which are computational expensive and is not easy to apply on large-scale data sets. Contrarily, the topological learning i.e. SOM method allows a projection of the dataset in low dimensional spaces that make it easy to use for very large datasets. The prototypes matrix weighted by the neighbourhood function will be used in this work to reduce the computational time of the clustering algorithm and to add the topological information to the final clustering result. We illustrate the power of this method with several real datasets. The results show a good quality of clustering results and a higher speed.