Mining Frequent Closed Itemsets for Association Rules

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision- making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Download Full-text

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.145.292 ◽

2011 ◽

Vol 145 ◽

pp. 292-296

Author(s):

Lee Wen Huang

Keyword(s):

Data Mining ◽

Association Rules ◽

Execution Time ◽

Hybrid Algorithm ◽

Hybrid Approach ◽

Market Basket Analysis ◽

Market Basket ◽

Large Databases ◽

Closed Itemsets ◽

Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

Download Full-text

Improved Privacy

Intelligent Information Technologies and Applications - Advances in Intelligent Information Technologies ◽

10.4018/978-1-59904-958-8.ch014 ◽

2011 ◽

pp. 295-316

Author(s):

K. Abumani ◽

R. Nedunchezhian

Keyword(s):

Data Mining ◽

Decision Making ◽

Empirical Analysis ◽

Time Complexity ◽

Strategic Decision ◽

Strategic Decision Making ◽

Sensitive Information ◽

Data Mining Techniques ◽

Novel Method ◽

Mining Tools

Data mining techniques have been widely used for extracting non-trivial information from massive amounts of data. They help in strategic decision-making as well as many more applications. However, data mining also has a few demerits apart from its usefulness. Sensitive information contained in the database may be brought out by the data mining tools. Different approaches are being utilized to hide the sensitive information. The proposed work in this article applies a novel method to access the generating transactions with minimum effort from the transactional database. It helps in reducing the time complexity of any hiding algorithm. The theoretical and empirical analysis of the algorithm shows that hiding of data using this proposed work performs association rule hiding quicker than other algorithms.

Download Full-text

Quality and Effectiveness of ERP Software

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Metrics and Models for Evaluating the Quality and Effectiveness of ERP Software ◽

10.4018/978-1-5225-7678-5.ch002 ◽

2020 ◽

pp. 28-52

Author(s):

Stephen Makau Mutua ◽

Raphael Angulu

Keyword(s):

Data Mining ◽

Decision Making ◽

Knowledge Discovery ◽

Strategic Decision ◽

Strategic Decision Making ◽

Data Repository ◽

Quality Information ◽

Erp System ◽

Entry Points ◽

Over Time

Over time, the adoption of ERP systems has been wide across many small, medium, and large organizations. An ERP system is supposed to inform the strategic decision making of the organization; therefore, the information drawn from the ERP system is as important as the data stored in it. Poor data quality affects the quality information in it. Data mining is used to discover trends and patterns of an organization. This chapter looks into the way of integrating these data mining into an ERP system. This is conceptualized in three crucial views namely the outer, inner, and the knowledge discovery view. The outer view comprises of the collection of various entry points, the inner view contains the data repository, and the knowledge discovery view offers the data mining component. Since the focus is data mining, the two strategies of supervised and unsupervised are discussed. The chapter then concludes by presenting the probable problems within which each of these two strategies (classification and clustering) can be put into place within the mining process of an ERP system.

Download Full-text

Expanding Data Mining Power with System Dynamics

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch166 ◽

2008 ◽

pp. 2688-2696

Author(s):

Edilberto Casado

Keyword(s):

Data Mining ◽

Decision Making ◽

Pattern Recognition ◽

System Dynamics ◽

Knowledge Discovery ◽

Strategic Decision ◽

Knowledge Discovery In Databases ◽

Strategic Decision Making ◽

The World ◽

Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

Download Full-text

Algorithms for Association Rule Mining

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch012 ◽

2011 ◽

pp. 76-84

Author(s):

Vasudha Bhatnagar ◽

Anamika Gupta ◽

Naveen Kumar

Keyword(s):

Data Mining ◽

Association Rule ◽

Association Rule Mining ◽

Strategic Decision ◽

Strategic Decision Making ◽

Rule Mining ◽

Massive Datasets ◽

Typical Application ◽

Mining Community ◽

Aggregate Query

Association Rule Mining (ARM) is one of the important data mining tasks that has been extensively researched by data-mining community and has found wide applications in industry. An Association Rule is a pattern that implies co-occurrence of events or items in a database. Knowledge of such relationships in a database can be employed in strategic decision making in both commercial and scientific domains. A typical application of ARM is market basket analysis where associations between the different items are discovered to analyze the customer’s buying habits. The discovery of such associations can help to develop better marketing strategies. ARM has been extensively used in other applications like spatial-temporal, health care, bioinformatics, web data etc (Hipp J., Güntzer U., Nakhaeizadeh G. 2000). An association rule is an implication of the form X ? Y where X and Y are independent sets of attributes/ items. An association rule indicates that if a set of items X occurs in a transaction record then the set of items Y also occurs in the same record. X is called the antecedent of the rule and Y is called the consequent of the rule. Processing massive datasets for discovering co-occurring items and generating interesting rules in reasonable time is the objective of all ARM algorithms. The task of discovering co-occurring sets of items cannot be easily accomplished using SQL, as a little reflection will reveal. Use of ‘Count’ aggregate query requires the condition to be specified in the where clause, which finds the frequency of only one set of items at a time. In order to find out all sets of co-occurring items in a database with n items, the number of queries that need to be written is exponential in n. This is the prime motivation for designing algorithms for efficient discovery of co-occurring sets of items, which are required to find the association rules. In this article we focus on the algorithms for association rule mining (ARM) and the scalability issues in ARM. We assume familiarity of the reader with the motivation and applications of association rule mining

Download Full-text

Expanding Data Mining Power with System Dynamics

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch204 ◽

2005 ◽

pp. 1155-1161

Author(s):

Edilberto Casado

Keyword(s):

Data Mining ◽

Decision Making ◽

Pattern Recognition ◽

System Dynamics ◽

Knowledge Discovery ◽

Strategic Decision ◽

Knowledge Discovery In Databases ◽

Strategic Decision Making ◽

The World ◽

Mathematical Techniques

Business intelligence (BI) is a key topic in business today, since it is focused on strategic decision making and on the search of value from business activities through empowering a “forward-thinking” view of the world. From this perspective, one of the most valuable concepts within BI is the “knowledge discovery in databases” or “data mining,” defined as “the process of discovering meaningful new correlations, patterns, and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (SPSS, 1997).

Download Full-text