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Batra, Shalini
- Analyzing the Social Networks Using Block Modeling Technique
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Authors
Affiliations
1 Thapar University, Patiala-147001, IN
1 Thapar University, Patiala-147001, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 6 (2011), Pagination: 340-344Abstract
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.- Ontological Approach to Integrate Semantically Related Databases
Abstract Views :174 |
PDF Views:1
Authors
Affiliations
1 Thapar University, Patiala-147001, IN
1 Thapar University, Patiala-147001, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 163-167Abstract
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.- Analyzing the Effect of Adding Noise on Compressed Textual Data
Abstract Views :213 |
PDF Views:2
Authors
Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 3 (2009), Pagination: 93-96Abstract
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.- Feature Selection for Text Clustering and Classification
Abstract Views :167 |
PDF Views:2
Authors
Affiliations
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
1 Thapar University, Patiala, IN
2 CSED, Thapar University, Patiala, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 3 (2009), Pagination: 97-101Abstract
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.- Visualizing the Domain in 3-Dimension Using Semantic Clustering
Abstract Views :174 |
PDF Views:2
Authors
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
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