DISCOVERY OF CAUSALITY POSSIBILITIES

2004 ◽  
Vol 18 (01) ◽  
pp. 63-73 ◽  
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.

Visual Data Mining for Discovering Association Rules

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.

Visual Data Mining for Discovering Association Rules

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.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

Internet Data Mining

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).

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

Research of Improved Attribute Reduction Algorithm Based on Data Mining of Rough Set

2014 ◽  
Vol 644-650 ◽  
pp. 2120-2123 ◽  
Author(s):  
De Zhi An ◽  
Guang Li Wu ◽  
Jun Lu
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Reduction Algorithm ◽  
The Core ◽  
Rules Extraction

At present there are many data mining methods. This paper studies the application of rough set method in data mining, mainly on the application of attribute reduction algorithm based on rough set in the data mining rules extraction stage. Rough set in data mining is often used for reduction of knowledge, and thus for the rule extraction. Attribute reduction is one of the core research contents of rough set theory. In this paper, the traditional attribute reduction algorithm based on rough sets is studied and improved, and for large data sets of data mining, a new attribute reduction algorithm is proposed.

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