A project report submitted in the partial fulfillment of the requirements For the award of the degree of
BACHELOR OF TECHNOLOGY
COMPUTER SCIENCE AND ENGINEERING
Under the esteemed guidance of
Department of Computer Science and Engineering
V.R.Siddhartha Engineering College
(Affiliated to Acharya Nagarjuna University)
Approved by AICTE- Accredited by NBA
This is to certify that the project report entitled “A New Method for Generating All Positive and Negative Association Rules” being submitted by
in partial fulfillment for the award of the Degree of Bachelor of Technology in Computer Science and Engineering to the Acharya Nagarjuna University is a record of bonafied work carried out under my guidance and supervision.
The results embodied in this project report have not been submitted to any other University or Institute for the award of any Degree or Diploma.
Dr.K.Srinivas,M.Tech,Ph.D Dr.V.Srinivas Rao,M.Tech,Ph.D Project Guide Head of the Department
Behind every achievement lies an unfathomable sea of gratitude of those who activated, without whom it would never have come into existence. To them we lay the words of gratitude imprinted with us.
We would like to thank our respected principal Dr.K.Mohan Rao and also our sincere thanks to Dr.V.Srinivasa Rao, Head of the Department , Computer Science and Engineering for their support through out our project.
It is our sincere obligation to thank my guide Dr.K.Srinivas,Professor,Department of CSE , for his timely valuable guidance and suggestions for this project work.
We would like to thank to my entire faculty who has given us the required model support in every situation of our engineering career, which helped us in completing this project.
Association Rule play very important role in recent scenario of data mining. But we have only generated positive rule, negative rule also useful in today data mining task. In this paper we are proposing “A new method for generating all positive and negative Association Rules” (NRGA).NRGA generates all association rules which are hidden when we have applied Apriori Algorithm. For representation of Negative Rules we are giving new name of this rules as like: CNR, ANR, and ACNR. In this paper we are also modify Correlation coefficient (CRC) equation, so all generate results are very promising. First we apply Apriori Algorithm for frequent item set generation and that is also generate positive rules, after on frequent item set we apply NRGA algorithm for all negative rules generation and optimize generated rules using Genetic Algorithm
LIST OF SCREENSiv
LIST OF FIGURESv
LIST OF TABLESvi
1.3LIMITATIONS IN EXISTING SYSTEM2
1.4 PROPOSED SYSTEM3
1.5ADVANTAGES OF PROPOSED SYSTEM3
2. SOFTWARE REQUIREMENT ANALYSIS4
2.1 PROBLEM ANALYSIS4
2.2.1 POSITIVE RULE GENERATION4
2.2.2 NEGATIVE RULE GENERATION5
3 SYSTEM DESIGN7
3.1.1 PURPOSE OF THE SYSTEM7
3.1.2 DESIGN GOALS7
3.2 USECASE DIAGRAM7
3.3 SEQUENCE DIAGRAM9