Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity

2021 ◽  
pp. 241-253
Author(s):  
Alexandar Vincent-Paulraj ◽  
Girvan Burnside ◽  
Frans Coenen ◽  
Munir Pirmohamed ◽  
Lauren Walker
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Sequential Association Rule Mining

Automatic Item Weight Generation for Pattern Mining and its Application

2013 ◽  
pp. 187-207 ◽  
Author(s):  
Yun Sing Koh ◽  
Russel Pears ◽  
Gillian Dobbie
Keyword(s):  
Uniform Distribution ◽  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Natural Variation ◽  
Pattern Mining ◽  
Weighting Scheme ◽  
Rule Mining ◽  
Real World Application

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

scholarly journals Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

2022 ◽  
Vol 1 ◽  
Author(s):  
Agostinetto Giulia ◽  
Sandionigi Anna ◽  
Bruno Antonia ◽  
Pescini Dario ◽  
Casiraghi Maurizio
Keyword(s):  
Machine Learning ◽  
16S Rrna ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Microbial Community Composition ◽  
Frequent Itemset ◽  
Supervised Machine Learning ◽  
Rule Mining ◽  
Microbiome Data

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

A grocery recommendation for off-line shoppers

2018 ◽  
Vol 42 (4) ◽  
pp. 468-481 ◽  
Author(s):  
Jae Kyeong Kim ◽  
Hyun Sil Moon ◽  
Byong Ju An ◽  
Il Young Choi
Keyword(s):  
Markov Chain ◽  
Association Rule ◽  
Association Rule Mining ◽  
Markov Chain Model ◽  
Personalized Recommendation ◽  
Chain Model ◽  
Rule Mining ◽  
Content Type ◽  
Sequential Association Rule Mining ◽  
Purchase Patterns

Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

Optimal Inventory Classification using Data Mining Techniques

2016 ◽  
pp. 236-255 ◽  
Author(s):  
Reshu Agarwal ◽  
Mandeep Mittal ◽  
Sarla Pareek
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Consumer Behavior Analysis ◽  
Relationship Extraction ◽  
Using Data ◽  
Research Studies

Data mining has long been used in relationship extraction from large amount of data for a wide range of applications such as consumer behavior analysis in marketing. Data mining techniques, such as classification, association rule mining, temporal association rule mining, sequential pattern mining, decision trees, and clustering, have attracted attention of several researchers. Some research studies have also extended the usage of this concept in inventory management to determine the optimal economic order quantity. Yet, not many research studies have considered the application of the data mining approach on inventory classification to predict the most profitable items which is also a significant factor to the manager for optimal inventory control. In this chapter, three different cases for inventory classification based on loss rule is presented. An example is illustrated to validate the results.

Automatic Item Weight Generation for Pattern Mining and its Application

2011 ◽  
Vol 7 (3) ◽  
pp. 30-49 ◽  
Author(s):  
Yun Sing Koh ◽  
Russel Pears ◽  
Gillian Dobbie
Keyword(s):  
Uniform Distribution ◽  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Natural Variation ◽  
Pattern Mining ◽  
Weighting Scheme ◽  
Rule Mining ◽  
Real World Application

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

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.

scholarly journals SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 395-399 ◽  
Author(s):  
S. Khoshahval ◽  
M. Farnaghi ◽  
M. Taleai
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Activity Patterns ◽  
Handheld Devices ◽  
Trajectory Data ◽  
Hidden Information ◽  
Rule Mining ◽  
User Activity ◽  
Gps Trajectories

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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