Arul Siromoney

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.

The generic rough set inductive logic programming (gRS--ILP) model

The example semantics of Inductive Logic Programming (ILP) systems is said to be in a rough setting when the consistency and completeness criteria cannot both be fulfilled together, because the evidence, background knowledge and declarative bias are such that any induced hypothesis cannot distinguish between some of the positive and negative examples. The gRS-ILP model (generic Rough Set Inductive Logic Programming model) provides a theoretical foundation in this rough setting for an ILP system to induce hypotheses that are used to say that an example is definitely positive, or definitely negative. An illustrative example using Progol is presented. Results are presented of GOLEM experiments using the data set for drug design for Alzheimers disease and other experiments using Progol on mutagenesis data and transmembrane domain data.

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.

Consistency and Completeness in Rough Sets

Consistency and completeness are defined in the context of rough set theory and shown to be related to the lower approximation and upper approximation, respectively. A member of a composed set (union of elementary sets) that is consistent with respect to a concept, surely belongs to the concept. An element that is not a member of a composed set that is complete with respect to a concept, surely does not belong to the concept. A consistent rule and a complete rule are useful in addition to any other rules learnt to describe a concept. When an element satisfies the consistent rule, it surely belongs to the concept, and when it does not satisfy the complete rule, it surely does not belong to the concept. In other cases, the other learnt rules are used. The results in the finite universe are extended to the infinite universe, thus introducing a rough set model for the learning from examples paradigm. The results in this paper have application in knowledge discovery or learning from database environments that are inconsistent, but at the same time demand accurate and definite knowledge. This study of consistency and completeness in rough sets also lays the foundation for related work at the intersection of rough set theory and inductive logic programming.

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.