Data Mining and Knowledge Engineering

Pratipalsinh Zala ¹, Hiren Kotadiya ¹, Sanjay D. Bhanderi ¹

Affiliations
1 Department of Computer Engineering, Marwadi Education Foundation, Rajkot, IN

Abstract

Currently there is explosive growth of information in all the fields of marketing, science, technology etc. Frequent pattern mining is the process of knowledge discovery from immense database. Number of research papers has been discovered on frequent pattern mining. Now a day frequent pattern mining on single processor or node has become bottleneck because, millions of transactions emerge as a result of large data entities. In this paper we present a survey of various frequent pattern mining algorithms which have been proposed on parallel computing environment. Parallel computing has been an efficient way for frequent pattern mining like massive computational task. We have made a survey on very important and well known papers regarding parallel frequent pattern mining. In these papers various traditional methods have been taken as base and developed a novel approach on them parallel. Apriori and FP-tree have been very famous and efficient frequent pattern mining algorithms. But they are not sufficient enough in this era of data mining. A new approach of Inverted matrix has been discussed regarding parallel environment. This approach also overcomes various inefficiencies of the conventional approaches of Apriori and FP-growth algorithms. We have presented a table with parameters which evaluate all approaches represented here in parallel computing environment.

Keywords

Apriori, FP-Tree, Frequent Itemset, Parallel Computing.

Full Text

     

Nishant J. Shah ¹, Sanjay D. Bhanderi ²

Affiliations
1 Gujarat Technological University, IN
2 Department of Computer Engineering, Marwadi Education Foundation Group of Institutions, IN

Abstract

In the current age of information overloading, it is very hard to find out relevant information. Innovations of search engine which helped users to find out relative information. But, the information could not be personalized by these engines. So, Recommendation System introduced for solving for this problem. The main aim of recommendation system is providing suggestions to a user. The suggestions is based on the user's choices  like what items to buy, what music to listen to, what online news to read, or which is the best movie.
Recommendation system is mainly used for to find out accuracy, diversity; flexibility. Diversity is divided into two parts. Aggregate diversity and individual diversity. Aggregate diversity is the diversity which provides the number of items because of the many of users like it. Individual diversity is the diversity which provide the accurate result based on the user's choice. Recommendation systems are usually divided into three categories: Content based method, collaborative method and hybrid method.

Keywords

Accuracy, Diversity, Recommendation System.

Full Text

     

Manthan J. Vyas ¹, Sanjay D. Bhanderi ²

Affiliations
1 Marwadi Education Foundation Group of Institutions, Gujarat Technological University, Ahmedabad, Gujarat, IN
2 Department of Computer Engineering, Marwadi Education Foundation Group of Institutions, Gujarat Technological University, Rajkot, IN

Abstract

Text mining is the process of obtaining interesting patterns or knowledge from text documents. The most often used type of data in the WWW is text. Text mining is used to extract interesting knowledge from unstructured text data. Pre-processing is a very important phase in the text mining process. Text mining framework includes two components, text refining and knowledge distillation. This paper is about pre-processing for text mining in English and Gujarati language. There is very less work done for text mining in Gujarati language. It is very challenging task as Gujarati is very rich in morphology, it gives rise to a very large number of word forms and feature spaces. Some pre-processing techniques in Gujarati are introduced in this paper.

Keywords

Pre-Processing, Stop-Words, Stemming, Text Mining.

Full Text