The Selection of Economic Horticulture for Suitable Planting in Ubon Ratchathani Province by Data Mining Techniques

Chutchai Kaewta, Chanankarn Saengprasan


          The objectives of this research were to create models and develop a program that selects suitable economic horticulture for the cultivated areas in Ubon Ratchathani Province using 2-steps data mining techniques: K-means clustering technique and Decision tree classification technique. The secondary data in 2015-2017 were used in this research. The input variables were soil data, economic horticultural data, climate data, and area. The class variable was the 14 types of economic horticulture. The result of the K-means clustering found that Ubon Ratchathani Province was classified in the third cluster with a total of 207 districts in 15 provinces. The results of the decision tree classifications found that 1) the suitable economic horticulture for planting in all districts of Ubon Ratchathani Province were rambutan, rubber, oil palm, and citrus with an accuracy of 75.77%, 71.42%, 69.23%, and 58.84%, respectively. 2) The suitable economic horticultures for planting in some districts of Ubon Ratchathani Province were mangosteen, lychee, tangerine, durian, longan with an accuracy of 87.37%, 87.37%, 86.24%, 85.73%, and 85.23% respectively. When the model of horticultural selection was used to develop a program in web application. The satisfied level of a sample of the farmer to a program that selects suitable economic horticulture for the cultivated areas in Ubon Ratchathani Province was high level.             


Keywords :  data mining ; clustering;  classification

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