Data Mining and Knowledge Engineering

Shalini Batra ¹

Affiliations
1 Thapar University, Patiala-147001, IN

Abstract

With the advent of the Internet, social networks have grown enormously and Social Network Analysis (SNA) has come up as an important field for research. Social networks are represented as graphs where each node called an actor or vertex in a graph a is derived using various social network modeling techniques. One of the well known technique called 'block modeling' groups vertices into clusters and determine the relations between these clusters using matrices as computational tools. It is grounded on different structural concepts like equivalence and positions which are related to the theoretical concepts of social role and role sets. In this paper, the intent is to generate social network using various network tools and analyze the relationship between participants using block modeling techniques. The data, generated in binary form, has been analyzed and visualized with varying cluster sizes.

Keywords

Block Modeling, Clustering, Social Networks, Social Networks Analysis.

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Sugandha Lohar ¹, Shalini Batra ¹

Affiliations
1 Thapar University, Patiala-147001, IN

Abstract

Interoperability and integration of data sources are becoming ever more challenging issues with the increase in both the amount of data and the number of data producers. Interoperability not only has to resolve the differences in data structures, it also has to deal with semantic heterogeneity. Taking semantically heterogeneous databases as the prototypical situation, this paper describes how ontology (in the traditional metaphysical sense) can contribute to delivering a more efficient and effective process of matching by providing a framework for the analysis, and so the basis for a methodology. It delivers not only a better process for matching, but the process also gives a better result.

Keywords

Interoperability, Ontology, Semantic Heterogeneity.

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Sudesh Kumar ¹, Shalini Batra ²

Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN

Abstract

Compression is one of the techniques for better utilization of storage devices, resulting in saving of storage space.This paper addresses compression by using the technique called Normalized Compression Distance (NCD). The Normalized Compression Distance is based on algorithmic complexity developed by Kolmogorov, called Normalized Information Distance.Normalized Compression Distance can be used to cluster objects of any kind, such as music, texts, or gene sequences (microarray classification). The NCD between two binary strings is defined in terms of compressed sizes of the two strings and of their concatenation; it is designed to be an effective approximation of the non computable but universal Kolmogorov distance between two strings. This paper studies the influence of noise on the normalized compression distance, a measure based on the use of compressors to compute the degree of similarity of two files. This influence is approximated by a first order differential equation which gives rise to a complex effect, which explains the fact that the NCD may give values greater than 1. Finally, the analyzing the effect of adding noise on compressed textual data and findings are that NCD performs well even in the presence of quite high noise levels by using CompLearn Toolkit.

Keywords

Kolmogorov Complexity, Normalized Information Distance, Normalized Compression Distance, Compression.

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Kamlesh Dhayal ¹, Sudesh Kumar ¹, Shalini Batra ²

Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN

Abstract

The quality of the data is one of the most important factors influencing the performance of any classification or clustering algorithm. The attributes defining the feature space of a given data set can often be inadequate, which make it difficult to discover useful information or desired output. However, even when the original attributes are individually inadequate, it is often possible to combine such attributes in order to construct new ones with greater predictive power. Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, and noise from data to improving result comprehensibility. This paper addresses the task of feature selection for clustering and classification. Here we give a comparative study of variety of classification methods, including Naive Bayes, J48 etc.

Keywords

Classification, Clustering, Feature Selection, Machine Learning.

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Sanjay Madan ¹, Purnima Ahuja ², Shalini Batra ³

Affiliations
1 Computer Science and Engineering, Thapar University, Patiala-147004, IN
2 Information Technology, Banasthali University, Banasthali, Rajasthan, IN
3 Computer Science and Engineering Department, Thapar University, Patiala-147004, IN

Abstract

To understand the software source code lots of approaches have been developed and many of them concern to the program structural information but this results in the loss of domain semantic crucial information contained in the text or symbols of source code. To understand software as a whole, we need to enrich these approaches with conceptual insights gained from the domain semantics. This paper proposes the mapping of domain to the code using the information retrieval techniques to use linguistic information, such as identifier names and comments in source code. Here we introduce the concept of Semantic Clustering, and an algorithm to group source artifacts based on how the synonymy and polysemy is related. Based on semantic similarity automatic labeling of the program code is done after detecting the clusters, and is visually explore in 3-Dimension format. The most important feature of theis approach is that it works at the source code textual level which makes it language independent. The approach correlates the semantics with structural information applies at different levels of abstraction (e.g.packages, classes, methods).

Keywords

Information Retrieval, Latent Semantic Indexing, Semantic Clustering, Software Reverse Engineering.

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