Autonomous Temporal 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.

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Present State-of-The-Art of Association Rule Mining Algorithms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2202.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6398-6405

Keyword(s):

Data Mining ◽

Association Rule ◽

Association Rule Mining ◽

State Of The Art ◽

Synthetic Data ◽

Data Sets ◽

Evolutionary Analysis ◽

Rule Mining ◽

Transaction Database ◽

Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

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ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

Jurnal Riset Informatika ◽

10.34288/jri.v2i1.68 ◽

2019 ◽

Vol 2 (1) ◽

pp. 31-36

Author(s):

Arfianto Darmawan ◽

Titin Kristiana

Keyword(s):

Data Mining ◽

Strong Association ◽

Apriori Algorithm ◽

Business Unit ◽

Savings And Loans ◽

Transaction Data ◽

Existing Problems ◽

A Value ◽

Transaction Database ◽

Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

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Mining Allocating Patterns in Investment Portfolios

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch007 ◽

2009 ◽

pp. 110-135

Author(s):

Yanbo J. Wang ◽

Xinwei Zheng ◽

Frans Coenen

Keyword(s):

Data Mining ◽

Portfolio Management ◽

Association Rule ◽

Common Type ◽

Experimental Results ◽

Transaction Database ◽

Investment Portfolios ◽

The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

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A Novel and Improved Apriori Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.721.543 ◽

2014 ◽

Vol 721 ◽

pp. 543-546 ◽

Cited By ~ 1

Author(s):

Dong Juan Gu ◽

Lei Xia

Keyword(s):

Data Mining ◽

Association Rules ◽

Inner Product ◽

Apriori Algorithm ◽

Product Operation ◽

Classical Algorithm ◽

Transaction Database ◽

The Given ◽

To Receive ◽

Improved Algorithm

Apriori algorithm is the classical algorithm in data mining association rules. Because the Apriori algorithm needs scan database for many times, it runs too slowly. In order to improve the running efficiency, this paper improves the Apriori algorithm based on the Apriori analysis. The improved idea is that it transforms the transaction database into corresponding 0-1 matrix. Whose each vector and subsequent vector does inner product operation to receive support. And comparing with the given minsupport, the rows and columns will be deleted if vector are less than the minsupport, so as to reduce the size of the rating matrix, improve the running speeding. Because the improved algorithm only needs to scan the database once when running, therefore the running speeding is more quickly. The experiment also shows that this improved algorithm is efficient and feasible.

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Multi-level Utility Mining: Retrieval of High Utility Itemsets in a Transaction Database

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2019.04.003 ◽

2019 ◽

Vol 76 ◽

pp. 268-282

Author(s):

C Sivamathi ◽

S Vijayarani

Keyword(s):

Utility Mining ◽

Transaction Database ◽

Multi Level ◽

High Utility ◽

High Utility Itemsets

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Association Rule-Based Novel Incremental Updating Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.317-319.1868 ◽

2011 ◽

Vol 317-319 ◽

pp. 1868-1871

Author(s):

Jian Hong Li

Keyword(s):

Association Rules ◽

Association Rule ◽

Research Topic ◽

Massive Data ◽

Important Research ◽

Incremental Updating ◽

Rule Based ◽

Important Research Topic ◽

Transaction Database ◽

Novel Algorithm

This paper focuses on an important research topic in data mining (DM) which heavily replies on the association rules. In order to deal with the maintenance issues within the background of the static transaction database, there are some minor changes to minimum support and confidence coefficient. A novel algorithm based on incremental updated is proposed, which is termed as NIUA (Novel Incremental Updating Algorithm). IUA uses association rules to mining the database, aiming at finding the potential information or finding the reasons from massive data.

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