Frequent Itemsets as Descriptors of Textual Records

2019 ◽  
pp. 35-45 ◽  
Author(s):  
Ayoub Bokhabrine ◽  
Ismaïl Biskri ◽  
Nadia Ghazzali
Keyword(s):  
Frequent Itemsets ◽  
Textual Records

scholarly journals New Descriptors of Textual Records: Getting Help from Frequent Itemsets

2020 ◽  
Vol 07 (04) ◽  
pp. 355-372
Author(s):  
Ayoub Bokhabrine ◽  
Ismaïl Biskri ◽  
Nadia Ghazzali
Keyword(s):  
Social Activity ◽  
Numerical Data ◽  
Frequent Itemsets ◽  
Clustering Methods ◽  
Support Set ◽  
Textual Data ◽  
Different Types ◽  
Lexical Quality ◽  
Textual Records

The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we implemented a platform named “IDETEX” capable of extracting itemsets from textual data and using it for the experimentation in different types of clustering methods, such as [Formula: see text]-Medoids and Hierarchical clustering. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.

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

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.

Frequent Itemsets Mining of SCADA Data Based on FP-Growth Algorithm

2020 ◽  
Author(s):  
Xiaolei Ma ◽  
Yongguang Li ◽  
Ran Liu ◽  
Yanjun Zhang ◽  
Liya Ma ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Frequent Itemsets Mining

A Novel Approach for Finding Frequent Itemsets in Data Stream

2013 ◽  
Vol 28 (3) ◽  
pp. 217-241 ◽  
Author(s):  
B. Chandra ◽  
Shalini Bhaskar
Keyword(s):  
Data Stream ◽  
Frequent Itemsets ◽  
Novel Approach
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