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Mining Frequent Itemsets without Candidate Generation using Optical Neural Network

Published on None 2011 by Divya Bhatnagar, Neeru Adlakha, A. S. Saxena
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Artificial Intelligence Techniques - Novel Approaches & Practical Applications
Foundation of Computer Science USA
AIT - Number 4
None 2011
Authors: Divya Bhatnagar, Neeru Adlakha, A. S. Saxena
5bc6e967-b4b2-48d5-89d8-b273da98c4bb

Divya Bhatnagar, Neeru Adlakha, A. S. Saxena . Mining Frequent Itemsets without Candidate Generation using Optical Neural Network. Artificial Intelligence Techniques - Novel Approaches & Practical Applications. AIT, 4 (None 2011), 26-30.

@article{
author = { Divya Bhatnagar, Neeru Adlakha, A. S. Saxena },
title = { Mining Frequent Itemsets without Candidate Generation using Optical Neural Network },
journal = { Artificial Intelligence Techniques - Novel Approaches & Practical Applications },
issue_date = { None 2011 },
volume = { AIT },
number = { 4 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /specialissues/ait/number4/2846-227/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%A Divya Bhatnagar
%A Neeru Adlakha
%A A. S. Saxena
%T Mining Frequent Itemsets without Candidate Generation using Optical Neural Network
%J Artificial Intelligence Techniques - Novel Approaches & Practical Applications
%@ 0975-8887
%V AIT
%N 4
%P 26-30
%D 2011
%I International Journal of Computer Applications
Abstract

We propose an efficient technique for mining frequent itemsets in large databases making use of Optical Neural Network Model. It eliminates the need to generate candidate sets and joining them for finding frequent itemsets for association rule mining. Since optical neural network performs many computations simultaneously, the time complexity is very low as compared to other data mining techniques. The data is stored in such a way that it minimizes space complexity to a large extent. This paper focuses on how this model can be helpful in generating frequent patterns for various applications. Appropriate methods are also designed that reduces the number of database scans to just one. It is fast, versatile, and adaptive. It discovers frequent patterns by using the best features of data mining, optics and neural networks

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Index Terms

Computer Science
Information Sciences

Keywords

Optical Neural Network Weight matrix Electro-optical Vector multiplier Electro-optical Vector multiplier