An Efficient Method for Discretizing Continuous Attributes

In this paper the authors present a novel method for finding optimal split points for discretization of continuous attributes. Such a method can be used in many data mining techniques for large databases. The method consists of two major steps. In the first step search space is pruned using a bisecting region method that partitions the search space and returns the point with the highest information gain based on its search. The second step consists of a hill climbing algorithm that starts with the point returned by the first step and greedily searches for an optimal point. The methods were tested using fifteen attributes from two data sets. The results show that the method reduces the number of searches drastically while identifying the optimal or near-optimal split points. On average, there was a 98% reduction in the number of information gain calculations with only 4% reduction in information gain.

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Visual Data Mining for Discovering Association Rules

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch125 ◽

2008 ◽

pp. 2105-2120

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Visual Data Mining for Discovering Association Rules

Business Applications and Computational Intelligence ◽

10.4018/978-1-59140-702-7.ch011 ◽

2011 ◽

pp. 209-226

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Post-Processing for Rule Reduction Using Closed Set

Post-Mining of Association Rules ◽

10.4018/978-1-60566-404-0.ch005 ◽

2009 ◽

pp. 81-99 ◽

Cited By ~ 2

Author(s):

Huawen Liu ◽

Jigui Sun ◽

Huijie Zhang

Keyword(s):

Data Mining ◽

Efficient Method ◽

End Users ◽

Rule Base ◽

Main Character ◽

Post Processing ◽

Closed Set ◽

Rule Management ◽

Large Databases ◽

Dependent Relation

In data mining, rule management is getting more and more important. Usually, a large number of rules will be induced from large databases in many fields, especially when they are dense. This, however, directly leads to the gained knowledge hard to be understood and interpreted. To eliminate redundant rules from rule base, many efforts have been made and various efficient and outstanding algorithms have been proposed. However, end-users are often unable to complete a mining task because there are still insignificant rules. Thus, it becomes apparent that an efficient technique is needed to discard useless rules as more as possible, without information lossless. To achieve this goal, in this paper we propose an efficient method to filter superfluous rules from knowledge base in a post-processing manner. The main character of our method lies in that it eliminates redundancy of rules by dependent relation, which can be discovered by closed set mining technique. Their performance evaluations show that the compression degree achieved by our proposed method is better and its efficiency is also higher than those of other techniques.

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A search space reduction methodology for data mining in large databases

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2008.04.003 ◽

2009 ◽

Vol 22 (1) ◽

pp. 57-65 ◽

Cited By ~ 28

Author(s):

Angel Kuri-Morales ◽

Fátima Rodríguez-Erazo

Keyword(s):

Data Mining ◽

Search Space ◽

Space Reduction ◽

Large Databases ◽

Search Space Reduction

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DISCOVERY OF CAUSALITY POSSIBILITIES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003058 ◽

2004 ◽

Vol 18 (01) ◽

pp. 63-73 ◽

Cited By ~ 1

Author(s):

LAWRENCE MAZLACK

Keyword(s):

Data Mining ◽

Association Rules ◽

Joint Probability ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Large Databases ◽

Very Large Databases ◽

Predictive Relationships ◽

Strength Of Association

Determining causality has been a tantalizing goal throughout human history. Proper sacrifices to the gods were thought to bring rewards; failure to make suitable observations were thought to lead to disaster. Today, data mining holds the promise of extracting unsuspected information from very large databases. Methods have been developed to build association rules from large data sets. Association rules indicate the strength of association of two or more data attributes. In many ways, the interest in association rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, association rules only calculate a joint probability; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful. Our goal is to explore causality in the data mining context.

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Rapid Privacy Preserving Algorithm for Large Databases

Database Technologies ◽

10.4018/978-1-60566-058-5.ch061 ◽

2009 ◽

pp. 1050-1061

Author(s):

K. Anbumani ◽

R. Nedunchezhian

Keyword(s):

Data Mining ◽

Decision Making ◽

Time Complexity ◽

Strategic Decision ◽

Strategic Decision Making ◽

Sensitive Information ◽

Data Mining Techniques ◽

Large Databases ◽

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.

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Internet Data Mining

Internet Management Issues ◽

10.4018/978-1-930708-21-1.ch015 ◽

2011 ◽

pp. 236-253

Author(s):

Kuldeep Kumar ◽

John Baker

Keyword(s):

Data Mining ◽

Credit Card ◽

Web Mining ◽

Large Data ◽

Customer Segmentation ◽

Large Data Sets ◽

The Internet ◽

Data Sets ◽

Large Databases ◽

The Web

Data mining has emerged as one of the hottest topics in recent years. It is an extraordinarily broad area and is growing in several directions. With the advancement of the Internet and cheap availability of powerful computers, data is flooding the market at a tremendous pace. However, the technology for navigating, exploring, visualizing and summarizing large databases are still in their infancy. The quantity and diversity of data available to make decisions has increased dramatically during the past decade. Large databases are being built to hold and deliver these data. Data mining is defined as the process of seeking interesting or valuable information within large data sets. Some examples of data mining applications in the area of management science are analysis of direct-mailing strategies, sales data analysis for customer segmentation, credit card fraud detection, mass customization, etc. With the advancement of the Internet and World Wide Web, both management scientists and interested end-users can get large data sets for their research from this source. The Web not only contains a vast amount of useful information, but also provides a powerful infrastructure for communication and information sharing. For example, Ma, Liu and Wong (2000) have developed a system called DS-Web that uses the Web to help data mining. A recent survey on Web mining research can be seen in the paper by Kosala and Blockeel (2000).

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A Survey on Preparing Data Sets for Data Mining Analysis using Horizontal Aggregations in SQL

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i4/0199 ◽

2017 ◽

Vol 7 (5) ◽

pp. 172-176

Author(s):

Prashant B. Rajole ◽

Keyword(s):

Data Mining ◽

Data Sets ◽

Data Mining Analysis

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Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

Current Bioinformatics ◽

10.2174/1574893615666200204154358 ◽

2021 ◽

Vol 15 (8) ◽

pp. 912-926

Author(s):

Ge Zhang ◽

Pan Yu ◽

Jianlin Wang ◽

Chaokun Yan

Keyword(s):

Feature Selection ◽

Chemical Reaction ◽

Information Gain ◽

Feature Selection Method ◽

Search Space ◽

Neighborhood Search ◽

Biomedical Data ◽

Chemical Reaction Optimization ◽

Search Mechanism ◽

Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

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