Advances in Information Mining

1-4

Authors: Anandhavalli M., Suraj Kumar Sudhanshu, Ayush Kumar , Ghose M.K

In general the rule generated by association rule mining algorithms like priori, partition, pincer-search, incremental, border algorithm etc, does not consider negation occurrence of the attribute in them and also these rules have only one attribute in the consequent part. By using Genetic Algorithm (GAs) the system can predict the rules which contain negative attributes in the generated rules along with more than one attribute in consequent part. The major advantage of using GAs in the discovery of prediction rules is that they perform global search and its complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the possible optimized rules from given data set using genetic algorithm.

11-13

Authors: Chitra Desai , Manisha Patil , Sahebrao Shinde

The ability to handle complex data has made object oriented database gain a wide attention in the field of scientific research and engineering. Using object-oriented database for scientific data in it has several characteristics that should be considered for storing and retrieving it for future use. As this information needs to be exchanged and shared, XML in itself is emerging as a standard for information exchange over heterogeneous systems. The object oriented database structure designed using UML standard can be transformed into XML DTD. The XML DTD can further be used for obtaining DTD graph, which is further, used for extracting object oriented database schema by applying set of rules for class definition. This paper takes an example XML schema and transforms it into relational database schema. As object oriented database can be looked for more complex type of data handling compared to relational database, the XML schema is transformed to XML DTD and is used for representing object oriented database schema.

1-4

Authors: Sangya Pundir , Somnath Tagore

Many new structural patterns have been discovered in diverse biological, social and information networks. One of them are metabolic networks, the most widely studied large scale networks in biology, known to have a power law degree distribution and the exponent _ is observed to be the same for all species. However, empirical evidence elucidating the nature of the process that gives rise to such structure is lacking this far. In this paper we review facts about power law distribution as relevant to metabolic networks. In particular we concentrate on the evolutionary and other implications of such a power law distribution.