scholarly journals Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

2021 ◽  
Vol 2 (2) ◽  
pp. 56
Author(s):  
Trisna Yuniarti ◽  
Dahliyah Hayati
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Oil Palm ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Mining Technique ◽  
Mining Technique ◽  
The Government ◽  
Clustering Data ◽  
Medium Potential

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.

Knowledge Structure and Data Mining Techniques

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.

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.

Knowledge Structure and Data Mining Techniques

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.

Knowledge Structure and Data Mining Techniques

2011 ◽  
pp. 946-954 ◽  
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.

Knowledge Structure and Data Mining Techniques

2011 ◽  
pp. 523-529
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.

From Churn Models to Churn Solution

2014 ◽  
pp. 190-208
Keyword(s):  
Data Mining ◽  
Analytical Method ◽  
Time Dependent ◽  
Data Sets ◽  
Data Mining Technique ◽  
Model Classification ◽  
Mining Technique ◽  
Expert Models ◽  
Segmentation Models ◽  
Current Business

This chapter explains churn model classification, describes techniques for developing predictive churn models, and describes how to build churn segmentation models, churn time-dependent models, and expert models for churn reduction. Analysts (readers) are shown a holistic picture for churn modeling and presented an analytical method with techniques described as elements that could be used for building a final churn solution depending on current business problems and expected outputs. There are numerous ways for designing final churn models (solutions). The first criteria is to find solutions that will be in line with business needs. The problem is not applying some data mining technique; the problem is in choosing and preparing appropriate data sets. Applied techniques should show holistic solution pictures for churn, which are explainable and understandable for making decisions, which will help in churn understanding and churn mitigation.

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