Evaluation of the spatio-temporal of soil salinity variation using data mining approach

2011 ◽  
Author(s):  
Khatereh Polous ◽  
Abbas Farshad ◽  
Manouchehr Zarinkafsh ◽  
Mohammad Hassan Roozitalab
Keyword(s):  
Data Mining ◽  
Soil Salinity ◽  
Salinity Variation ◽  
Data Mining Approach ◽  
Spatio Temporal ◽  
Using Data

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

Towards a predictive model for opal exploration using a spatio-temporal data mining approach

2013 ◽  
Vol 60 (2) ◽  
pp. 217-229 ◽  
Author(s):  
A. S. Merdith ◽  
T. C. W. Landgrebe ◽  
A. Dutkiewicz ◽  
R. D. Müller
Keyword(s):  
Data Mining ◽  
Predictive Model ◽  
Temporal Data Mining ◽  
Temporal Data ◽  
Data Mining Approach ◽  
Spatio Temporal

scholarly journals ANALYSIS OF THESIS ADVISING PATTERN IN MASTER PROGRAM ON FACULTY OF AGRICULTURE IPB USING DATA MINING APPROACH

Edulib ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 194
Author(s):  
Lilis Syarifah ◽  
Imas Sukaesih Sitanggang ◽  
Pudji Muljono
Keyword(s):  
Data Mining ◽  
Upland Rice ◽  
Future Research ◽  
Rule Mining ◽  
Related Item ◽  
Text Documents ◽  
Master Program ◽  
R Language ◽  
Data Mining Approach ◽  
Using Data

The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.

Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

2010 ◽  
Vol 37 (7) ◽  
pp. 5259-5264 ◽  
Author(s):  
Seyed Mohammad Seyed Hosseini ◽  
Anahita Maleki ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Customer Loyalty ◽  
Data Mining Approach ◽  
Using Data
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