Ramp: Fast Frequent Itemset Mining with Efficient Bit-Vector Projection Technique

04/21/2009
by   Shariq Bashir, et al.
0

Mining frequent itemset using bit-vector representation approach is very efficient for dense type datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. To check the efficiency of our bit-vector projection technique, we present a new frequent itemset mining algorithm Ramp (Real Algorithm for Mining Patterns) build upon our bit-vector projection technique. The performance of the Ramp is compared with the current best (all, maximal and closed) frequent itemset mining algorithms on benchmark datasets. Different experimental results on sparse and dense datasets show that mining frequent itemset using Ramp is faster than the current best algorithms, which show the effectiveness of our bit-vector projection idea. We also present a new local maximal frequent itemsets propagation and maximal itemset superset checking approach FastLMFI, build upon our PBR bit-vector projection technique. Our different computational experiments suggest that itemset maximality checking using FastLMFI is fast and efficient than a previous will known progressive focusing approach.

READ FULL TEXT
research
04/21/2009

FastLMFI: An Efficient Approach for Local Maximal Patterns Propagation and Maximal Patterns Superset Checking

Maximal frequent patterns superset checking plays an important role in t...
research
04/21/2009

HybridMiner: Mining Maximal Frequent Itemsets Using Hybrid Database Representation Approach

In this paper we present a novel hybrid (arraybased layout and vertical ...
research
04/21/2009

Fast Algorithms for Mining Interesting Frequent Itemsets without Minimum Support

Real world datasets are sparse, dirty and contain hundreds of items. In ...
research
02/21/2019

Performance study of distributed Apriori-like frequent itemsets mining

In this article, we focus on distributed Apriori-based frequent itemsets...
research
11/07/2017

Grafting for Combinatorial Boolean Model using Frequent Itemset Mining

This paper introduces the combinatorial Boolean model (CBM), which is de...
research
03/21/2022

An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints

Constraint Programming is a powerful paradigm to model and solve combina...
research
09/24/2021

An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration

Frequent Directions, as a deterministic matrix sketching technique, has ...

Please sign up or login with your details

Forgot password? Click here to reset