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Year 2004: Volume I Issue II


Paper Title: An Efficient Hierarchical Clustering Algorithm for Protein Sequences. (
Authors: P. A. Vijaya, M. Narasimha Murty and D. K. Subramanian

Area: Bioinformatics, Clustering
Abstract: Clustering is the division of data into groups of similar objects. The main objective of this unsuper-vised learning technique is to find a natural grouping or meaningful partition by using a distance or similarity function. Clustering is mainly used for dimensionality reduction, prototype selection/abstractions for pattern classification, data reorganization and indexing and for detecting outliers and noisy patterns. Clustering techniques are applied in pattern classification schemes, bioinformatics, data mining, web mining, biometrics, 'document processing, remote sensed data analysis, biomedical data analysis, etc., in which the data size is very large. In this paper, an efficient incremental clustering algorithm -'Leaders-Subleaders' - an extension of leader algorithm, suitable for protein sequences of bioinformatics is proposed for effective clustering and prototype selection for pattern classification. It is another simple and efficient technique to generate a hierarchical structure for finding the subgroups/subclusters within each cluster which may be used to find the family and subfamily relationships of protein sequences. The experimental results (classification accuracy using the prototypes obtained and the computation time) of the proposed algorithm are compared with that of leader based and nearest neighbour classifier (NNC) methods. It is found to be computationally efficient when compared to NNC. Classification accuracy obtained using the representatives generated by the Leaders-Subleaders method is found to be better than that of using leaders as representatives and it approaches to that of NNC if sequential search is used on the sequences from the selected subcluster. Even if more number of prototypes are generated, classification time is less as only a part of the hierarchical structure is searched in Leaders-Subleaders method.


Paper Title:
 A* Algebra for an Extended Object/Relational Model. (PDF)
Authors: S. Nait Bahloul, Y. Amghar, and M. Sayah

Area: Database
Abstract: The object relational data model presents both the advantage of Codd's relational calculus power and the characteristics of the object orientation. Two major approaches have been adopted to satisfy the requirements of new databases applications. A first approach integrates the object characteristics into the new data models with the specification of data constraints and the definition of interrogation language. The second one, called evolutionary approach, keeps Codd's data model enriching with adequate concepts for the coverage of current database applications. In this approach and comparatively with studies presented by Melton, Date and Darwen have proposed the foundations of the object relational model. So, A-algebra consisting of first order logic operators has been defined to express various classes of queries in object relational database. To contribute to the improvement of relational/object models and agebra this paper presents an extension of object relational model to new types generated by operators and the related A*- algebra. These operators, called Op, offer a means to specify domains as functions and permit consequently to increase the data model expressiveness. To support this extension, we propose a new data query language, or more precisely a logical data calculation A* as an adapatation of the A-algebra. Our A*-algebra contains algebraic operators which are able to support this new extension.


Paper Title:
 Numerical Strategies for the System of Second Order IVPs Using the RK-Butcher Algorithms. (PDF)

Area: Numerical Methods
Abstract: In this paper, a new method of analysis for second order initial value problems using the RKButcher algorithms is presented. To illustrate the effectiveness of the RK-Butcher algorithms, seven problems of different kinds have been considered and the solutions were obtained using the RK method based on Arithmetic mean (RKAM), Centroidal mean (RKCeM) and the RK-Butcher Algorithms and are compared with the exact solutions of the seven problems. Stability regions for the RK-Butcher algorithm, RKAM and RKCeM methods are presented. Error graphs for the second order initial value problems have been presented in a graphical form to show the efficiency of this RK-Butcher algorithm. This RK-Butcher algorithm can be easily implemented in a digital computer and the solution can be obtained for any length of time.

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