deepak garg phd, deepak garg ieee, deepak garg acm developing efficient algorithms for incremental...

Developing Efficient Algorithms

for Incremental

Mining of Sequential Patterns

A Thesis

Submitted in fulfillment of the

requirements for the award of the degree of

Doctor of Philosophy

Submitted by

Bhawna Mallick (Registration No: 950903027)

Under the supervision of

Dr. Deepak Garg Dr. P S Grover Associate Professor, Professor of Computer Science,

Computer Science and Engineering Guru Tegh Bahadur Institute of Technology,

Department, Thapar University, Guru Gobind Singh Indraprastha University,

Patiala – 147004 Delhi – 110006

Computer Science and Engineering Department

Thapar University,

Patiala-147004 (Punjab), India.

August 2013

To

My Family

CERTIFICATE

I hereby certify that the work which is being presented in this thesis entitled

DEVELOPING EFFICIENT ALGORITHMS FOR INCREMENTAL MINING OF

SEQUENTIAL PATTERNS, in fulfillment of the requirements for the award of

degree of DOCTOR OF PHILOSOPHY submitted in Computer Science and

Engineering Department (CSED), Thapar University, Patiala, Punjab, is an authentic

record of my own work carried out under the supervision of Dr. Deepak Garg and

Dr. P. S. Grover, and refers the work of other researchers, which are duly listed in

the reference section.

The matter presented in this thesis has not been submitted for the award of any

other degree of this or any other university.

(Bhawna Mallick)

Registration No. 950903027

This is to certify that the above statement made by the candidate is correct and true

to the best of my knowledge and belief.

(Deepak Garg) (P.S. Grover)

Associate Professor, Professor of Computer Science,

Computer Science and Engineering Guru Tegh Bahadur Institute of Technology,

Department, Thapar University, Guru Govind Singh Indraprastha University,

Patiala – 147004. Delhi – 110006.

Supervisor Supervisor

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ACKNOWLEDGEMENTS

It would not have been possible to write this doctoral thesis without the help and support

of the kind people around me, to only some of whom it is possible to give particular

mention here.

At the outset, this research endeavor has been only possible with the help,

support and able guidance of my supervisors, Dr. Deepak Garg and Dr. P.S. Grover. I am

highly indebted to Dr. P. S. Grover for his advice, encouragement and unsurpassed

knowledge of comparative linguistics. The good support, diligence and friendship of Dr.

Deepak Garg have been invaluable on both an academic and a personal level, for which I

am extremely grateful. It has indeed been a privilege to carry out this research work

under their guidance.

I would like to acknowledge the Director and Dean (R&D), Thapar University for

the academic and technical support which has been indispensable. I express my gratitude

to the Doctoral Committee comprising of Dr. Maninder Singh, Dr. R. K. Sharma and PG

Coordinator, Dr. Inderveer Chana for monitoring the progress and providing valuable

suggestions for improvement of my Ph. D. work from time to time. I also thank the

faculty and staff of Computer Science and Engineering Department, especially the Head

of Department, Dr. Maninder Singh in promoting a stimulating and welcoming academic

and cooperative environment.

I am grateful to the Director and Management, Galgotias College of Engineering

and Technology, Greater Noida, Uttar Pradesh for providing me resources and facilities

to carry out this research work. I would also like to thank my colleagues and friends for

their support and valuable suggestions throughout. I am thankful to my friends working

in corporate for providing necessary feedback and suggestions required for this research

work.

My hearty thanks to all the anonymous referees of several international research

journals in the field of intelligent data mining and fuzzy set theory. The research work

presented in this thesis report has accomplished the eminence due to the constructive

comments and ideas for improvement by them.

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I would like to thank many acclaimed researchers across the world for timely

contributing the reprints of their valuable research papers for my reference apropos this

thesis report. These include Dr. Lei Chang, Dr. C.H. Cheng, Dr T.P. Hong, Dr. Jiang Hua,

Dr. S. Piramuthu, Dr. Jong Bum Lee, Dr. S. K. Gupta, Dr. M.Y. Lin, Dr. Jian Pei, Dr. V.

Kumar, Dr. X. Wu, Dr. Mingming Zhou, and several others.

I express gratitude to my loving husband Nitin for his personal support and great

patience at all times. I acknowledge the constant cooperation from him without which

this work could not have been possible. My parents and sisters have given me their

unequivocal support throughout, as always, for which my mere expression of thanks

likewise does not suffice. I dedicate this thesis to my family.

Above all, I pay my reverence to the almighty God.

Date: July, 2013.

Place: Thapar University, Patiala. (Bhawna Mallick)

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ABSTRACT

There has been an increase in our capabilities of both generating and collecting

data with the progress of humanity and development of technology. The ultimate intent

of this massive data collection is to utilize it for different reasons that include process

efficiency, system automation and competitive benefits among others. This is achieved by

determining both implicit and explicit unidentified patterns in data that can direct the

process of decision making. Data mining is a process of finding rules and patterns that

describe the characteristic properties of the data. Sequential pattern mining is one of the

methods of data mining. Sequential pattern is a sequence of item sets that frequently

occur in a specific order; where all items in the same item set are supposed to have the

same transaction time value.

Sequential pattern mining is used in wide spectrum of areas, such as financial

data analysis of banks and other financial institutions, retail industry, customer shopping

history, goods transportation, consumption and services, telecommunication industry,

biological data analysis, scientific applications, network intrusion detection.

The sequential pattern mining algorithms, in general, result in enormous number

of interesting and uninteresting patterns. Therefore, the miner may have difficulty in

selecting the interesting/significant ones. The core idea behind this study is the

incorporation of specific constraints into the mining process. One another important

concern of sequential pattern mining methods is that they are based on assumption that

the data is centralized and static. For dynamic data addition and deletion in the

databases, these methods waste computational and input/output (I/O) resources. It is

undesirable to mine sequential patterns from scratch each time when a small set of

sequences grow, or when some new sequences are added, deleted or updated in the

database.

There is consistent outdating of the discovered sequential patterns because of the

incremental evolution of databases (progressive databases) over time. This necessitates

incremental mining of sequential patterns from progressive database so as to capture the

dynamic nature of data addition, deletion and updating. In present study, an extensive

study of the algorithms that perform discovery of constraint-based sequential patterns

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and incremental mining of sequential patterns available in the literature is done. On the

basis of the study; efficient algorithms for incremental mining of sequential patterns from

progressive database are proposed.

This study contributes to the literature as it identifies the approach and

constraints that can be used for developing algorithms for the incremental mining of

sequential patterns. The constraints have been selected to keep track of customer

purchasing history and further be used for customer value analysis for CRM. A COPRE

(COnstraint-based PREfix) framework is developed based on pattern growth approach

that can incorporate any number of constraints and used for any domain. The first three

proposed algorithms, ‚Constraint-based sequential pattern mining algorithm

incorporating compactness, length and monetary‛; ‚CFM-PrefixSpan algorithm‛ and

‚Progressive CFM-Miner algorithm‛ are based on this framework. The real-world

business and commercial databases consist of numerical and time-stamped data. We can

get more relevant rules from these databases by making use of fuzzy set theory to

minimize the sharp cut between intervals. Fuzzy sequential mining generate simple and

practical patterns which are close to human reasoning. So, the fourth algorithm,

‚Progreslattice-Miner (PLM) algorithm‛ is ba