V. Uma Maheswari

The Variable Precision Rough Set Model for Web Usage Mining

Web Knowledge Discovery and Data Mining includes discovery and leveraging different kinds of hidden patterns in web data. In this paper we mine web user access patterns and classify users using the Variable Precision Rough Set (VPRS) model. Certain user sessions of web access are positive examples and other sessions are negative examples. Cumulative graphs capture all known positive example sessions and negative example sessions. They are then used to identify the attributes that are used to form an equivalence relation. This equivalence relation is used for the s-probabilistic approximation classification of the VPRS model. An illustrative experiment is presented.

The Variable Precision Rough Set Inductive Logic Programming Model and Strings

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS‐ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS‐ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.

Studies on rough sets in multiple tables

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. This paper studies the use of Rough Set Theory and Variable Precision Rough Sets in a multi-table information system (MTIS). The notion of approximation regions in the MTIS is defined in terms of those of the individual tables. This is used in classifying an example in the MTIS, based on the elementary sets in the individual tables to which the example belongs. Results of classification experiments in predictive toxicology based on this approach are presented.

Rough Sets and Relational Learning

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. Recent research in Rough Set Theory includes the extension of Rough Set Theory to Relational Learning. A brief overview of the work in Rough Sets and Relational Learning is presented.

Extensions in Mobile Ad Hoc Routing Using Variable Precision Rough Sets

Mobile ad hoc networks are formed dynamically without any infrastructure and each node in the network is responsible for routing information. Rough set theory is a mathematical tool to deal with vagueness and uncertainty. Variable precision rough sets (VPRS) is a generalization of rough sets that allows for a controlled degree of misclassification. This paper proposes extensions in mobile ad hoc routing using VPRS. The performance of the proposed mobile ad hoc routing protocol is compared with that of an existing routing protocol.

Mobile Ad Hoc Routing Using Rough Set Theory

Mobile ad hoc networks are formed dynamically without any infrastructure and each node is responsible for routing information among them. Rough set theory is a mathematical tool to deal with vagueness and uncertainty. In this paper a routing protocol for mobile adhoc networks that uses rough set theory is proposed. The performance of the protocol is compared with that of an existing routing protocol.

The Variable Precision Rough Set Inductive Logic Programming Model and Web Usage Graphs

Inductive Logic Programming [42.1] is the research area formed at the intersection of logic programming and machine learning. Rough set theory [42.2], [42.3] defines an indiscernibility relation, where certain subsets of examples cannot be distinguished. The gRS-ILP model [42.4] introduces a rough setting in Inductive Logic Programming and describes the situation where the background knowledge, declarative bias and evidence are such that it is not possible to induce any logic program from them that is able to distinguish between certain positive and negative examples. Any induced logic program will either cover both the positive and the negative examples in the group, or not cover the group at all, with both the positive and the negative examples in this group being left out.

The Variable Precision Rough Set Inductive Logic Programming Model and Future Test Cases in Web Usage Mining

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) is the extension of the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended to the VPRSILP model by including features of the VPRS model. The VPRSILP model is further extended in this paper to include future test cases. This model is applied to Web usage mining and an illustrative experiment is presented.

Probability measures for prediction in multi-table infomation systems

Rough Set Theory is a mathematical tool to deal with vagueness and uncertainty. Rough Set Theory uses a single information table. Relational Learning is the learning from multiple relations or tables. This paper presents a new approach to the extension of Rough Set Theory to multiple relations or tables. The utility of this approach is shown in classification experiments in predictive toxicology.

MINING WEB USAGE GRAPHS USING EXAMPLE SEARCH SPACE

Web mining refers to the process of discovering potentially useful and previously unknown information or knowledge from web data. A graph-based framework is used for classifying Web users based on their navigation patterns. GOLEM is a learning algorithm that uses the example space to restrict the solution search space. In this paper, this algorithm is modified for the graph-based framework. GOLEM is appropriate in this application where the solution search space is very large. An experimental illustration is presented.