Cluster to User Profile Ontology Mapping

In this paper, we present an approach that uses cluster analysis techniques to extend the ontology of an E-learning domain. This approach is significantly different from any current information retrieval systems, it uses a global ontology model that represents the whole E-learning domain combined with clusters’ centroids vocabularies (terms) to extend the core ontology model. The most important advantage of clustering from the personalization perspective is that the clusters are later used as automatically constructed labels for each user profile. Hence, depending on the document collection and its evolution, both the user profiles and their underlying ontology labels are allowed to change or evolve accordingly. Our proposed approach has been implemented on the HyperMany-Media1 platform at Western Kentucky University, USA

Community Detection via Maximization of Modularity and Its Variants

In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Qds) which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first of them, referred to as Fine-tuned Q, is based on modularity (Q) while the second one is based on Modularity Density (Qds) and denoted as Fine-tuned Qds. Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy Q), Fine-tuned Q, and Fine-tuned Qds on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Qds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Qds can be applied to the communities detected by other algorithms to significantly improve their results

Analyse de grappe des données de catégories et de séquences étude et application à la prédiction de la faillite personnelle

Cluster analysis is one of the most important and useful data mining techniques, and there are many applications of cluster analysis in pattern extraction, information retrieval, summarization, compression and other areas. The focus of this thesis is on clustering categorical and sequence data. Clustering categorical and sequence data is much more challenging than clustering numeric data because there is no inherently meaningful measure of similarity between the categorical objects and sequences. In this thesis, we design novel efficient and effective clustering algorithms for clustering categorical data and sequence respectively, and we perform extensive experiments to demonstrate the superior performance of our proposed algorithm. We also explore the extent to which the use of the proposed clustering algorithms can help to solve the personal bankruptcy prediction problem. Clustering categorical data poses two challenges: defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this thesis, we view the task of clustering categorical data from an optimization perspective and propose a novel objective function. Based on the new formulation, we design a divisive hierarchical clustering algorithm for categorical data, named DHCC. In the bisection procedure of DHCC, the initialization of the splitting is based on multiple correspondence analysis (MCA). We devise a strategy for dealing with the key issue in the divisive approach, namely, when to terminate the splitting process. The proposed algorithm is parameter-free, independent of the order in which the data is processed, scalable to large data sets and capable of seamlessly discovering clusters embedded in subspaces. The prior knowledge about the data can be incorporated into the clustering process, which is known as semi-supervised clustering, to produce considerable improvement in learning accuracy. In this thesis, we view semi-supervised clustering of categorical data as an optimization problem with extra instance-level constraints, and propose a systematic and fully automated approach to guide the optimization process to a better solution in terms of satisfying the constraints, which would also be beneficial to the unconstrained objects. The proposed semi-supervised divisive hierarchical clustering algorithm for categorical data, named SDHCC, is parameter-free, fully automatic and effective in taking advantage of instance-level constraint background knowledge to improve the quality of the resultant dendrogram. Many existing sequence clustering algorithms rely on a pair-wise measure of similarity between sequences. Usually, such a measure is effective if there are significantly informative patterns in the sequences. However, it is difficult to define a meaningful pair-wise similarity measure if sequences are short and contain noise. In this thesis, we circumvent the obstacle of defining the pairwise similarity by defining the similarity between an individual sequence and a set of sequences. Based on the new similarity measure, which is based on the conditional probability distribution (CPD) model, we design a novel model-based K -means clustering algorithm for sequence clustering, which works in a similar way to the traditional K -means on vectorial data. Finally, we develop a personal bankruptcy prediction system whose predictors are mainly the bankruptcy features discovered by the clustering techniques proposed in this thesis. The mined bankruptcy features are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance

Scribe: A Clustering Approach To Semantic Information Retrieval

Information retrieval is the process of fulfilling a user?s need for information by locating items in a data collection that are similar to a complex query that is often posed in natural language. Latent Semantic Indexing (LSI) was the predominant technique employed at the National Institute of Standards and Technology?s Text Retrieval Conference for many years until limitations of its scalability to large data sets were discovered. This thesis describes SCRIBE, a modification of LSI with improved scalability. SCRIBE clusters its semantic index into discrete volumes described by high-dimensional extensions to computer graphics data structures. SCRIBE?s clustering strategy limits the number of items that must be searched and provides for sub-linear time complexity in the number of documents. Experimental results with a large, natural language document collection demonstrate that SCRIBE achieves retrieval accuracy similar to LSI but requires 1/10 the time

Hypergraph Partitioning in the Cloud

The thesis investigates the partitioning and load balancing problem which has many applications in High Performance Computing (HPC). The application to be partitioned is described with a graph or hypergraph. The latter is of greater interest as hypergraphs, compared to graphs, have a more general structure and can be used to model more complex relationships between groups of objects such as non-symmetric dependencies. Optimal graph and hypergraph partitioning is known to be NP-Hard but good polynomial time heuristic algorithms have been proposed. In this thesis, we propose two multi-level hypergraph partitioning algorithms. The algorithms are based on rough set clustering techniques. The first algorithm, which is a serial algorithm, obtains high quality partitionings and improves the partitioning cut by up to 71\% compared to the state-of-the-art serial hypergraph partitioning algorithms. Furthermore, the capacity of serial algorithms is limited due to the rapid growth of problem sizes of distributed applications. Consequently, we also propose a parallel hypergraph partitioning algorithm. Considering the generality of the hypergraph model, designing a parallel algorithm is difficult and the available parallel hypergraph algorithms offer less scalability compared to their graph counterparts. The issue is twofold: the parallel algorithm and the complexity of the hypergraph structure. Our parallel algorithm provides a trade-off between global and local vertex clustering decisions. By employing novel techniques and approaches, our algorithm achieves better scalability than the state-of-the-art parallel hypergraph partitioner in the Zoltan tool on a set of benchmarks, especially ones with irregular structure. Furthermore, recent advances in cloud computing and the services they provide have led to a trend in moving HPC and large scale distributed applications into the cloud. Despite its advantages, some aspects of the cloud, such as limited network resources, present a challenge to running communication-intensive applications and make them non-scalable in the cloud. While hypergraph partitioning is proposed as a solution for decreasing the communication overhead within parallel distributed applications, it can also offer advantages for running these applications in the cloud. The partitioning is usually done as a pre-processing step before running the parallel application. As parallel hypergraph partitioning itself is a communication-intensive operation, running it in the cloud is hard and suffers from poor scalability. The thesis also investigates the scalability of parallel hypergraph partitioning algorithms in the cloud, the challenges they present, and proposes solutions to improve the cost/performance ratio for running the partitioning problem in the cloud. Our algorithms are implemented as a new hypergraph partitioning package within Zoltan. It is an open source Linux-based toolkit for parallel partitioning, load balancing and data-management designed at Sandia National Labs. The algorithms are known as FEHG and PFEHG algorithms