Data Mining with Rule Induction

2005 ◽  
pp. 83-98
Author(s):  
Andrew Stranieri ◽  
John Zeleznikow
Keyword(s):  
Data Mining ◽  
Rule Induction

Designing Component-Based Heuristic Search Engines for Knowledge Discovery

2011 ◽  
pp. 68-88
Author(s):  
Craig M. Howard
Keyword(s):  
Operating System ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Search Engines ◽  
Rule Induction ◽  
Object Oriented Design ◽  
Modular Programming ◽  
Software Packages ◽  
Component Object ◽  
Microsoft Windows

The overall size of software packages has grown considerably over recent years. Modular programming, object-oriented design and the use of static and dynamic libraries have all contributed towards the reusability and maintainability of these packages. One of the latest methodologies that aims to further improve software design is the use of component-based services. The Component Object Model (COM) is a specification that provides a standard for writing software components that are easily interoperable. The most common platform for component libraries is on Microsoft Windows, where COM objects are an integral part of the operating system and used extensively in most major applications. This chapter examines the use of COM in the design of search engines for knowledge discovery and data mining using modern heuristic techniques and how adopting this approach benefits the design of a commercial toolkit. The chapter describes how search engines have been implemented as COM objects and how representation and problem components have been created to solve rule induction problems in data mining.

Optimization of Mean and Standard Deviation of Multiple Responses Using Patient Rule Induction Method

2018 ◽  
Vol 14 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Jin-Kyung Yang ◽  
Dong-Hee Lee
Keyword(s):  
Data Mining ◽  
Standard Deviation ◽  
Large Volume ◽  
Rule Induction ◽  
Induction Method ◽  
Multiple Responses ◽  
Case Examples ◽  
Product And Process ◽  
Patient Rule Induction Method ◽  
Operational Data

In product and process optimization, it is common to have multiple responses to be optimized. This is called multi-response optimization (MRO). When optimizing multiple responses, it is important to consider variability as well as mean of the multiple responses. The authors call this problem as extended MRO (EMRO) where both of mean and variability of the multiple responses are optimized. In this article, they propose a data mining approach to EMRO. In these days, analyzing a large volume of operational data is getting attention due to the development of data processing techniques. Traditional MRO methods takes a model-based approach. However, this approach has limitations when dealing with a large volume of operational data. The authors propose a particular data mining method by modifying patient rule induction method for EMRO. The proposed method obtains an optimal setting of the input variables directly from the operational data where mean and standard deviation of multiple responses are optimized. The authors explain a detailed procedure of the proposed method with case examples.

A dynamic rule-induction method for classification in data mining

2015 ◽  
Vol 2 (3) ◽  
pp. 233-253 ◽  
Author(s):  
Issa Qabajeh ◽  
Fadi Thabtah ◽  
Francisco Chiclana
Keyword(s):  
Data Mining ◽  
Rule Induction ◽  
Induction Method

scholarly journals Visualization Techniques for Data Mining

2008 ◽  
pp. 1623-1630
Author(s):  
Herna L. Viktor ◽  
Eric Paquet
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Large Data ◽  
Analysis Data ◽  
Rule Induction ◽  
Induction Programs ◽  
Data Repositories ◽  
Use Of The Internet ◽  
Visualization Techniques ◽  
Mining Tools

The current explosion of data and information, mainly caused by data warehousing technologies as well as the extensive use of the Internet and its related technologies, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, is aimed at addressing this need. Data mining automates the discovery of hidden patterns and relationships that may not always be obvious. Data mining tools include classification techniques (such as decision trees, rule induction programs and neural networks) (Han & Kamber, 2001), clustering algorithms and association rule approaches, amongst others.

Identification Through Data Mining

2011 ◽  
pp. 374-381
Author(s):  
Diego Liberati
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Bayesian Networks ◽  
Time Course ◽  
Minimum Description Length ◽  
General Purpose ◽  
Rule Induction ◽  
Induction Method ◽  
Principal Directions ◽  
Complementary Techniques

Four main general purpose approaches inferring knowledge from data are presented as a useful pool of at least partially complementary techniques also in the cyber intrusion identification context. In order to reduce the dimensionality of the problem, the most salient variables can be selected by cascading to a K-means a Divisive Partitioning of data orthogonal to the Principal Directions. A rule induction method based on logical circuits synthesis after proper binarization of the original variables proves to be also able to further prune redundant variables, besides identifying logical relationships among them in an understandable “if . then ..” form. Adaptive Bayesian networks are used to build a decision tree over the hierarchy of variables ordered by Minimum Description Length. Finally, Piece-Wise Affine Identification also provides a model of the dynamics of the process underlying the data, by detecting possible switches and changes of trends on the time course of the monitoring.

scholarly journals AN EFFICIENT APPROACH USING RULE INDUCTION AND ASSOCIATION RULE MINING ALGORITHMS IN DATA MINING

2013 ◽  
pp. 14-18
Author(s):  
KAPIL SHARMA ◽  
SHEVETA VASHISHT
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Work ◽  
Rule Induction ◽  
High Rate ◽  
Training Dataset ◽  
Rule Mining ◽  
Data Set ◽  
Fast Processing

In this research work we use rule induction in data mining to obtain the accurate results with fast processing time. We using decision list induction algorithm to make order and unordered list of rules to coverage of maximum data from the data set. Using induction rule via association rule mining we can generate number of rules for training dataset to achieve accurate result with less error rate. We also use induction rule algorithms like confidence static and Shannon entropy to obtain the high rate of accurate results from the large dataset. This can also improves the traditional algorithms with good result.

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