Hierarchial Approach for Frequent Closed Itemset Generation in Distributed Environment
نویسندگان
چکیده
منابع مشابه
Maximal frequent itemset generation using segmentation approach
Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This prob...
متن کاملA DIC-based Distributed Algorithm for Frequent Itemset Generation
A distributed algorithm based on Dynamic Itemset Counting (DIC) for generation of frequent itemsets is presented by us. DIC represents a paradigm shift from Apriori-based algorithms in the number of passes of the database hence reducing the total time taken to obtain the frequent itemsets. We exploit the advantage of Dynamic Itemset Counting in our algorithmthat of starting the counting of an i...
متن کاملFrequent Closed Itemset Mining Using Prefix Graphs
This paper presents PGMiner, a novel graph-based algorithm for mining frequent closed itemsets. Our approach consists of constructing a prefix graph structure and decomposing the database to variable length bit vectors, which are assigned to nodes of the graph. The main advantage of this representation is that the bit vectors at each node are relatively shorter than those produced by existing v...
متن کاملIndex-CloseMiner: An improved algorithm for mining frequent closed itemset
The set of frequent closed itemsets determines exactly the complete set of all frequent itemsets and is usually much smaller than the latter. This paper proposes an improved algorithm for mining frequent closed itemsets. Firstly, the index array is proposed, which is used for discovering those items that always appear together. Then, by using bitmap, an algorithm for computing index array is pr...
متن کاملA Prime Number Based Approach for Closed Frequent Itemset Mining in Big Data
Mining big datasets poses a number of challenges which are not easily addressed by traditional mining methods, since both memory and computational requirements are hard to satisfy. One solution for dealing with such requirements is to take advantage of parallel frameworks, such as MapReduce, that allow to make powerful computing and storage units on top of ordinary machines. In this paper, we a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2014
ISSN: 0975-8887
DOI: 10.5120/19001-0476