Rough clustering of sequential data

Authors:
Pradeep Kumar;P. Radha Krishna;Raju. S. Bapi;Supriya Kumar De
Affiliations:
Business Intelligence Lab, Institute for Development and Research in Banking Technology (IDRBT), 1, Castle Hills, Masab Tank, Hyderabad 500057, India and Computational Intelligence Lab, Department ...;Business Intelligence Lab, Institute for Development and Research in Banking Technology (IDRBT), 1, Castle Hills, Masab Tank, Hyderabad 500057, India;Computational Intelligence Lab, Department of Computer and Information Sciences, University of Hyderabad, Gachibowli, Hyderabad 500046, India;XLRI Jamshedpur, C.H. Area, Jamshedpur 831001, India
Venue:
Data & Knowledge Engineering
Year:
2007

Citing 19
Cited 12

Rough set approach to incomplete information systems

Information Sciences: an International Journal
Data clustering: a review

ACM Computing Surveys (CSUR)
Visualization of navigation patterns on a Web site using model-based clustering

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Rough-Fuzzy Hybridization: A New Trend in Decision Making

Rough-Fuzzy Hybridization: A New Trend in Decision Making
Data Mining: Introductory and Advanced Topics

Data Mining: Introductory and Advanced Topics
A Generalized Definition of Rough Approximations Based on Similarity

IEEE Transactions on Knowledge and Data Engineering
Rough-fuzzy functions in classification

Fuzzy Sets and Systems
Fast hierarchical clustering and its validation

Data & Knowledge Engineering
Time Complexity of Rough Clustering: GAs versus K-Means

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Survey of Longest Common Subsequence Algorithms

SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Case Generation Using Rough Sets with Fuzzy Representation

IEEE Transactions on Knowledge and Data Engineering
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
A Method of Web Search Result Clustering Based on Rough Sets

WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Indiscernibility-based clustering: rough clustering

IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
On generalizing rough set theory

RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Intrusion detection system using sequence and set preserving metric

ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics

Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm

Data & Knowledge Engineering
Fuzzy time series model based on probabilistic approach and rough set rule induction for empirical research in stock markets

Data & Knowledge Engineering
Fast Single-Link Clustering Method Based on Tolerance Rough Set Model

RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
SVM ensemble intrusion detection model based on rough set feature reduct

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A web page usage prediction scheme using sequence indexing and clustering techniques

Data & Knowledge Engineering
A software development tool for improving quality of service in distributed database systems

IIT'09 Proceedings of the 6th international conference on Innovations in information technology
The incremental method for fast computing the rough fuzzy approximations

Data & Knowledge Engineering
Review:

The Knowledge Engineering Review
Tolerance rough set theory based data summarization for clustering large datasets

Transactions on rough sets XIV
Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach

Knowledge-Based Systems
A New Similarity Metric for Sequential Data

International Journal of Data Warehousing and Mining
Detection of HTTP-GET attack with clustering and information theoretic measurements

FPS'12 Proceedings of the 5th international conference on Foundations and Practice of Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users.