Hybrid Temporal Mining for Finding Out Frequent Itemsets in Temporal Databases Using Clustering and Bit Vector Methods

2011 ◽  
pp. 245-255
Author(s):  
M. Krishnamurthy ◽  
A. Kannan ◽  
R. Baskaran ◽  
G. Bhuvaneswari
Keyword(s):  
Frequent Itemsets ◽  
Temporal Databases ◽  
Temporal Mining ◽  
Bit Vector

scholarly journals Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases

2011 ◽  
Vol 3 ◽  
pp. 513-523 ◽  
Author(s):  
M. Krishnamurthy ◽  
A. Kannan ◽  
R. Baskaran ◽  
M. Kavitha
Keyword(s):  
Frequent Itemsets ◽  
Temporal Databases ◽  
Mining Algorithm ◽  
Bit Vector

Mining Frequent Itemsets Based on A Vertical Bit-Vector Dot-Product CBD-Tree

2012 ◽  
Vol 7 (23) ◽  
pp. 393-399
Author(s):  
Quanzhu Yao ◽  
Yubing Zhang ◽  
Jiulong Zhang
Keyword(s):  
Frequent Itemsets ◽  
Dot Product ◽  
Bit Vector ◽  
Mining Frequent Itemsets

Discovering Frequent Itemsets an Improved Algorithm of Bit Vector and Graph

2014 ◽  
Vol 989-994 ◽  
pp. 3747-3750
Author(s):  
Nai Li Liu
Keyword(s):  
Frequent Itemsets ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Bit Vector ◽  
Mining Frequent Itemsets ◽  
Improved Algorithm

Because of the weakness of traditional Apriori algorithm, this paper presents an improved algorithm for mining frequent itemsets, which constructs bit vector and graph, the algorithm deletes node and the adjacent edges according to the number of node’s edges, which need traverse graph to generate candidate itemsets and verify candidate itemset by bit vector. Experimental results show that the improved algorithm has better efficiency than Apriori algorithm.

Frequent itemsets grouping algorithm based on Hash list

2013 ◽  
Vol 33 (11) ◽  
pp. 3045-3048
Author(s):  
Hongmei WANG ◽  
Ming HU
Keyword(s):  
Frequent Itemsets ◽  
Grouping Algorithm

A Decision Procedure for Bit-Vector Arithmetic

1998 ◽  
Author(s):  
Clark W. Barrett ◽  
David L. Dill ◽  
Jeremy R. Levitt
Keyword(s):  
Decision Procedure ◽  
Bit Vector

scholarly journals A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream

2021 ◽  
Vol 16 (2) ◽  
pp. 1-30
Author(s):  
Guangtao Wang ◽  
Gao Cong ◽  
Ying Zhang ◽  
Zhen Hai ◽  
Jieping Ye
Keyword(s):  
Frequency Estimation ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Closure Property ◽  
Frequent Itemset Mining ◽  
Itemset Mining ◽  
Minimum Value ◽  
Downward Closure ◽  
Bounded Size

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel k -Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.

On How Bit-Vector Logic Can Help Verify LTL-based Specifications

2020 ◽  
pp. 1-1
Author(s):  
Mohammad Mehdi Pourhashem Kallehbasti ◽  
Matteo Giovanni Rossi ◽  
Luciano Baresi
Keyword(s):  
Bit Vector
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