Hybrid Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

The oil palm is the most productive plantation product in Indonesia. Government strategies and policies related to oil palm plantations continue to be carried out considering that the plantation area is increasing every year. Segmentation of oil palm plantations based on area, production, and productivity aims to identify groups of potential oil palm plantations in the territory of Indonesia. This segmentation can provide consideration in formulating strategies and policies that will be made by the government. The segmentation method for grouping oil palm plantations uses the K-Means Clustering Data Mining technique with 3 clusters specified. Data mining stages start from data collection until representation is carried out, where 34 data sets are collected, only 25 data sets can be processed further. The results of this grouping obtained three plantation segments, namely 72% of the plantation group with low potential, 20% of the plantation group with medium potential, and 8% of the plantation group with high potential.

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Knowledge Structure and Data Mining Techniques

Knowledge Management ◽

10.4018/978-1-59904-933-5.ch072 ◽

2011 ◽

pp. 874-882

Author(s):

Rick L. Wilson ◽

Peter A. Rosen ◽

Mohammad Saad Al-Ahmadi

Keyword(s):

Data Mining ◽

Neural Networks ◽

Inductive Learning ◽

Knowledge Structure ◽

Statistical Techniques ◽

Data Sets ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Mining Technique ◽

Learning Techniques

Considerable research has been done in the recent past that compares the performance of different data mining techniques on various data sets (e.g., Lim, Low, & Shih, 2000). The goal of these studies is to try to determine which data mining technique performs best under what circumstances. Results are often conflicting—for instance, some articles find that neural networks (NN) outperform both traditional statistical techniques and inductive learning techniques, but then the opposite is found with other datasets (Sen & Gibbs, 1994; Sung, Chang, & Lee, 1999: Spangler, May, & Vargas, 1999). Most of these studies use publicly available datasets in their analysis, and because they are not artificially created, it is difficult to control for possible data characteristics in the analysis. Another drawback of these datasets is that they are usually very small.

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From data to knowledge mining

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006040900016x ◽

2009 ◽

Vol 23 (4) ◽

pp. 427-441 ◽

Cited By ~ 6

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.

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GRAPH-BASED HIERARCHICAL CONCEPTUAL CLUSTERING

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213001000441 ◽

2001 ◽

Vol 10 (01n02) ◽

pp. 107-135 ◽

Cited By ~ 14

Author(s):

ISTVAN JONYER ◽

LAWRENCE B. HOLDER ◽

DIANE J. COOK

Keyword(s):

Data Mining ◽

Knowledge Discovery ◽

Structural Information ◽

Clustering Algorithms ◽

Unstructured Data ◽

Conceptual Clustering ◽

Data Mining Technique ◽

Mining Technique ◽

Discovery System

Hierarchical conceptual clustering has proven to be a useful, although greatly under-explored data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides the advantages of both approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as compare SUBDUE to earlier clustering algorithms. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.

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Knowledge Structure and Data Mining Techniques

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch002 ◽

2008 ◽

pp. 9-17

Author(s):

Rick L. Wilson ◽

Peter A. Rosen ◽

Mohammad Saad Al-Ahmadi

Keyword(s):

Data Mining ◽

Neural Networks ◽

Inductive Learning ◽

Knowledge Structure ◽

Statistical Techniques ◽

Data Sets ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Mining Technique ◽

Learning Techniques

Considerable research has been done in the recent past that compares the performance of different data mining techniques on various data sets (e.g., Lim, Low, & Shih, 2000). The goal of these studies is to try to determine which data mining technique performs best under what circumstances. Results are often conflicting—for instance, some articles find that neural networks (NN) outperform both traditional statistical techniques and inductive learning techniques, but then the opposite is found with other datasets (Sen & Gibbs, 1994; Sung, Chang, & Lee, 1999: Spangler, May, & Vargas, 1999). Most of these studies use publicly available datasets in their analysis, and because they are not artificially created, it is difficult to control for possible data characteristics in the analysis. Another drawback of these datasets is that they are usually very small.

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