scholarly journals A Framework for Incremental Mining of Interesting Temporal Association Rules

2015 ◽  
Vol 131 (8) ◽  
pp. 28-33
Author(s):  
Ahmed Sultan
Keyword(s):  
Association Rules ◽  
Temporal Association ◽  
Incremental Mining

An efficient algorithm for incremental mining of temporal association rules

2010 ◽  
Vol 69 (8) ◽  
pp. 800-815 ◽  
Author(s):  
Tarek F. Gharib ◽  
Hamed Nassar ◽  
Mohamed Taha ◽  
Ajith Abraham
Keyword(s):  
Association Rules ◽  
Efficient Algorithm ◽  
Temporal Association ◽  
Incremental Mining

scholarly journals Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

2019 ◽  
Vol 8 (12) ◽  
pp. 5853-5861
Keyword(s):  
Association Rules ◽  
Execution Time ◽  
Search Space ◽  
Frequent Itemsets ◽  
Customer Behavior ◽  
Temporal Association ◽  
Incremental Mining ◽  
Buying Behavior ◽  
Memory Space ◽  
Variant Database

In retail business, customers’ behavior analytics is a study of customers’ buying behavior for a better understanding of customer needs to be able to provide service accordingly. The buying behavior is majorly influenced by the preferences of a customer. However, preferences of a customer change over a period of time due to various factors like change in income, taste, culture or newer products, etc. Understanding these changes in customer behavior is a very challenging task especially in a dynamic, ever-changing environment. There are various customer behavior mining models and techniques available in the data mining domain that are designed to work on static and dynamic databases. The traditional incremental mining techniques consider all the previous datasets in order to update the patterns. However, in a dynamic database, the size of the database grows with every update. To mine customers’ behavior in a time-variant database, the re-mining of the updated database is required that further increases processing cost in terms of execution time and memory space with every update. The purpose of this paper is to propose a method that can analyze the changes in customers’ behavior in time-variant databases without mining all the transactions. In this paper, an optimized incremental technique is proposed that utilizes temporal association rule mining in a time-variant database for mining customer behavioral patterns in an updated database. The proposed algorithm named ‘Autoregressive Moving Average model-based Incremental Temporal Association Rules Mining (ARMA-ITARM)’ utilizes the ARMA model to substantially reduce the database and maintains temporal frequent patterns in the updated database. Inspired by sliding window and pre-large concepts, the algorithm utilizes past frequent itemsets and probable frequent itemsets from customers’ purchased history along with frequent itemsets and probable frequent itemsets that reduce search space. Consequently, the entire database is scanned only once to count the frequency of occurrence of a few candidate itemsets. In effect, execution time memory need of the algorithm is very small. Experimental results demonstrate that our proposed technique performs better over recent techniques like ITARM, SWF, etc

scholarly journals IMTAR: Incremental Mining of General Temporal Association Rules

2010 ◽  
Vol 6 (2) ◽  
pp. 163-176 ◽  
Author(s):  
Anour F.A. Dafa-Alla ◽  
Ho-Sun Shon ◽  
Khalid E.K. Saeed ◽  
Minghao Piao ◽  
Un-Il Yun ◽  
...  
Keyword(s):  
Association Rules ◽  
Temporal Association ◽  
Incremental Mining

IMIDB: An Algorithm for Indexed Mining of Incremental Databases

2017 ◽  
Vol 26 (1) ◽  
pp. 69-85
Author(s):  
Mohammed M. Fouad ◽  
Mostafa G.M. Mostafa ◽  
Abdulfattah S. Mashat ◽  
Tarek F. Gharib
Keyword(s):  
Data Structure ◽  
Association Rules ◽  
Efficient Algorithm ◽  
Performance Comparison ◽  
Incremental Mining ◽  
Itemset Mining ◽  
Large Databases ◽  
Transactional Databases ◽  
Dynamic Databases ◽  
Database Size

AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

Fast Algorithms for Temporal Association Rules in a Large Database

2005 ◽  
Vol 277-279 ◽  
pp. 287-292 ◽  
Author(s):  
Lu Na Byon ◽  
Jeong Hye Han
Keyword(s):  
Electronic Commerce ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Large Database ◽  
Running Time ◽  
Smoothing Filter ◽  
Personalized Services ◽  
Temporal Association Rule ◽  
Transaction Database

As electronic commerce progresses, temporal association rules are developed by time to offer personalized services for customer’s interests. In this article, we propose a temporal association rule and its discovering algorithm with exponential smoothing filter in a large transaction database. Through experimental results, we confirmed that this is more precise and consumes a shorter running time than existing temporal association rules.

Mining temporal association rules with frequent itemsets tree

2018 ◽  
Vol 62 ◽  
pp. 817-829 ◽  
Author(s):  
Ling Wang ◽  
Jianyao Meng ◽  
Peipei Xu ◽  
Kaixiang Peng
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Temporal Association

Visualizing constraint-based temporal association rules

2001 ◽  
Vol 15 (5) ◽  
pp. 401-410 ◽  
Author(s):  
PANU KORPIPÄÄ
Keyword(s):  
Association Rules ◽  
Three Dimensional ◽  
Temporal Association ◽  
Hot Strip Mill ◽  
Virtual Model ◽  
Rule Mining ◽  
Continuous Processes ◽  
Hot Strip ◽  
Mining Tool ◽  
Over Time

When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

Closed-Itemset Incremental-Mining Problem

2011 ◽  
pp. 150-153
Author(s):  
Luminita Dumitriu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Incremental Mining ◽  
Minimum Support ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

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