DynTARM: An In-Memory Data Structure for Targeted Strong and Rare Association Rule Mining over Time-Varying Domains

2013 ◽  
Author(s):  
Jennifer Lavergne ◽  
Ryan Benton ◽  
Vijay Raghavan ◽  
Alaaeldin Hafez
Keyword(s):  
Data Structure ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Varying ◽  
Rule Mining ◽  
Rare Association ◽  
Over Time

scholarly journals Frequent item set mining using normalized FP-growth algorithm

2018 ◽  
Vol 7 (1.7) ◽  
pp. 59
Author(s):  
N K. Manikandan ◽  
D Manivannan
Keyword(s):  
Data Structure ◽  
Association Rule ◽  
Association Rule Mining ◽  
Hamming Distance ◽  
Data Access ◽  
Main Memory ◽  
Inverted Index ◽  
Rule Mining ◽  
High Dimension Data ◽  
Index Mapping

As the volume of data and its storage schemes are increasing drastically, the knowledge discovery from these huge volume of heterogeneous and high dimension data emerges as an essential process. Number of algorithms for data association analysis has been introduced considering time and main memory requirements. However this algorithms get completed when the items and records grows extremely high. In this paper we have created a data structure that can be reused without modifying the schema. So the aim of this work is to make an efficient association rule mining independent of the algorithm being selected.Our data structure make data access much faster by simplifying and reorganizing them by implementing shuffling strategy using hamming distance and inverted index mapping. In this work we explore our algorithm’s efficiency by using many datasets containing millions of records in it. We increased the runtime orders of the magnitude and reduced the auxiliary and main memory requirements. 

scholarly journals Cluster Centric Technique for Association Rule Mining(C-ARM) For High Precision and Coverage in Web Page Recommendation

2019 ◽  
Vol 8 (6) ◽  
pp. 1732-1735
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Computation Time ◽  
Web Pages ◽  
User Group ◽  
Web Page ◽  
Rule Mining ◽  
Modern Times ◽  
Over Time

Association Rule Mining (ARM) is known for its popularity and efficiency in the data mining domain. Over the recent years, the amount of data that gets accumulated in the internet is getting increased exponentially over time. The data available so are stored in online and are retrieved when a user requests for the same through key words with the help of a search engine. The important task of the search engines are to present the appropriate web pages that an user is expecting and in the modern times, The need of the hour is to recommend web pages to the users that he is interested in. This made the web page recommendation an important and vital task. Although many of the researchers are in the preliminary task of developing such systems, we in this research propose a recommendation model in which different users are interested upon a common item or domain by using the ARM concept. The data patterns that are in common are identified using the ARM and further these are clustered on a form of hierarchy .The clusters makes the recommendation system to easily identify the user group and based on the group, the pages are recommended, The experimental analysis are discussed and found to be efficient than the available methods in terms of computation time and reliability.

scholarly journals A New Approach on Rare Association Rule Mining

2012 ◽  
Vol 53 (3) ◽  
pp. 1-6 ◽  
Author(s):  
N. Hoque ◽  
B. Nath ◽  
D. K. Bhattacharyya
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
New Approach ◽  
Rare Association

scholarly journals Study to Determine Adverse Diseases Pattern using Rare Association Rule Mining

2020 ◽  
pp. 519-526
Author(s):  
Keerti Shrivastava ◽  
Varsha Jotwani
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Security Applications ◽  
Rare Association ◽  
The Common ◽  
Set Up ◽  
Original Motivation

Data mining is a method for finding patterns from repositories that remain hidden, unknown but fascinating. It has resulted in a number of strategies and emphasizes the detection of patterns to identify patterns that occur frequently, seldom and rarely. With their implementations, the work has improved the efficiency of the techniques. Yet typical methods for data mining are limited to databases with static behavior. The first move was to investigate similarities between the common objects through association rules mining. The original motivation for the search for these guidelines was the consumers ' shopping patterns in transaction data for supermarkets. This attempts to classify combinations of items or items that influence the presence likelihood of other items or items in a transaction. The request for rare association rule mining has improved in current years. The identification of unusual data patterns is critical, including medical, financial, or security applications. This survey seeks to give an analysis of rare pattern mining strategies, which in general, comprehensive and constructed. We discuss the issues in the quest for unusual rules using conventional association principles. Because mining rules for rare associations are not well known, special foundations still need to be set up.

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