Text Clustering for Natural Language Applications

SYllias Chali and Soufiane Noureddine

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Slim Ben Saoud, Amel Jaoua and Narjès Bellamine-Ben Saoud

Flexible Manufacturing Systems (FMS) based on Automated Guided Vehicles (AGV) have emerged as highly competitive manufacturing technologies of the last decade. However, their design and control are complex tasks since the decision problems related to the different system parameters such as sizing and scheduling are usually shown as NP-hard. Nowadays, computer simulation is one of the most commonly used methods for solving these problems. Now, we present a complete simulation tool which allows the design, analysis and control of FMS based on AGV. This tool is composed of three modules: (1) A simulation module which allows the estimation of the user introduced configuration. (2) An optimization module which is based on a meta-heuristic: the simulated annealing. This module is coupled with the simulation one in order to obtain an Executive Information System that is able to generate an optimal configuration. (3) A reactive control system which is based on the concept of real-time simulation and allows dynamic dispatching and routing of different systems inside the global FMS. The different developed modules have been validated by using a typical automated manufacturing system.

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An Approach for the Satisfiability Problem via Exterior Penalty Optimization

We present an iterative algorithm that promises to achieve a satisfactory solution for the central problem of computational logic and complexity theory. Clear evidence is given that the algorithm, in some circumstances, should perform well in practice though its implementation is still lacking. The method we use is optimization. The particular version of satisfiability we focus on is the exact satisfiability problem (XSAT), which is known to be NP-complete. The study presents the detailed algorithm and discusses correctness and efficiency issues.

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An Agent-Based Testbed for Simulating Large Scale Accident Rescue Heuristics

The scope of this study is to present our study of the complex social problem of large scale accident rescue by applying an agent-based approach. Our field of study concerns situations involving a large number of victims over a wide area (which may or not be hostile) and where rescuers have to act rapidly to rescue the greatest number of victims in the shortest time by optimizing both their human and material resources. Based on real life observations and rescue plans on one side and designing new rescuing strategies on the other side we have built a generic and interactive user-friendly simulator. Modeling and simulation provide us with a virtual environment where we can easily develop and test a large number of “what-if” heuristic scenarios of different rescue organizations. These organizations may be compared and assessed in order to find efficient configurations and strategies for organizing a rescue.

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Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm

Xiaohui Cui and Thomas E. Potok

There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize and organize the information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. The K-means algorithm is the most commonly used partitional clustering algorithm because it can be easily implemented and is the most efficient one in terms of the execution time. The major problem with this algorithm is that it is sensitive to the selection of the initial partition and may converge to a local optima. In this study, we present a hybrid Particle Swarm Optimization (PSO)+K-means document clustering algorithm that performs fast document clustering and can avoid being trapped in a local optimal solution as well. For comparison purpose, we applied the PSO+K-means, PSO, K-means and other two hybrid clustering algorithms on four different text document datasets. The number of documents in the datasets range from 204 to over 800 and the number of terms range from over 5000 to over 7000. The results illustrate that the PSO+K-means algorithm can generate the most compact clustering results than other four algorithms.

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A New Metric for Geometric Model Based Cache Invalidation of Location Dependent Data in Mobile Environment

Mobile computing as compared to traditional computing paradigms enables clients to have unrestricted mobility while maintaining network connections. Data management in this paradigm poses new challenging problems to the data base community. Location Dependent Information Services (LDIS) is an emergent application in this area where information provided to users depends on their current locations. Data caching at mobile clients play a key role in data management due to its ability to improve system performance and overcome availability limitations. Spatial data cached in the mobile clients may become invalid because of the movement of the client. Cache Invalidation schemes aims to keep data consistency between the client’s cache and the server. To maintain consistency of LDD in cache, valid scope of that data item is identified and stored along with it in the client’s cache. In this study, we focus on the selection procedure of finding best suitable candidate for valid scope (i.e., best suitable sub polygon of a given polygon) and propose a generalized algorithm which selects the best suitable candidate for valid scope. We compare its performance with the existing algorithms. More over, we also introduce a new metric FA and an algorithm CEFAB which tries to improve the performance by considering the user movement pattern and speculation about its future access.

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A Heuristic Reduct Computation Approach by Attributes Weighting for Rough Set Based Classification

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Integration Techniques to Build a Data Warehouse using Heterogeneous Data Sources

This work describes the construction of a data warehouse by the integration of heterogeneous relational, object-relational and XML data (complex data). In fact, developing intelligent tools for the integration of information extracted from multiple heterogeneous sources is a challenging issue to effectively exploit the numerous sources available in global information systems. Due to the heterogeneity of the sources, various languages of interrogation and different data models are used for the warehouses. Thus, the construction of the latters can be reached by several manners. Our work is based on the extraction of the inter-schema relationships between the sources. Related to this, a global schema was generated and the views of the data warehouse were constructed. All these stages, proposed in this work were implemented by the use of a functional prototype.

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M. Haydari, M. M. Moksin, N. Yahya, W. M. M. Yunus and V. I. Grozescu

Data mining has recently attracted attention as a set of efficient techniques that can discover patterns from huge data. More recent advancements in collecting massive evolving data streams created a crucial need for dynamic data mining. In this paper, we present a genetic algorithm based on a new representation mechanism that allows several phenotypes to be simultaneously expressed to different degrees in the same chromosome. This gradual multiple expression mechanism can offer a simple model for a multiploid representation with self-adaptive dominance, including co-dominance and incomplete dominance. Based on this model, we also propose a data mining approach that considers the data as a reflection of a dynamic environment and investigate a new evolutionary approach based on continuously mining non-stationary data sources that do not fit in main memory. Preliminary experiments are performed on real Web clickstream data.

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