Fuzzy-based knowledge discovery from heterogeneous data in planting systems for elderly LOHAS

Hung Chih Hsueh, Jung Yi Jiang, Jen Sheng Tsai, Wen Hao Tsai, Kuan Rong Lee, Yau Hwang Kuo

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a knowledge discovery method based on the fuzzy set theory to help elders with plant cultivation. Initially, the fuzzy sets are constructed by using the feature selection and statistical interval estimation. The min-max inference and the center of gravity defuzzification method are then used to output a candidate pattern set. Finally,a pattern discovery is adopted to obtain the patterns from the candidate set for the cultivation suggestions by considering the frequency weight and user's experience. In order to demonstrate the performance of our method in planting systems, we conduct a clicks-and-mortar cultivation platform, namely Eden Garden, for the elderly lifestyles of health and sustainability (LOHAS). The experimental results show that the accuracy rate of our knowledge discovery method can reach up to 85%.Moreover, the results of the LOHAS index scale table present that the happiness of the elders is increasing while the elders are using our proposed method.

Original languageEnglish
Pages (from-to)45-53
Number of pages9
JournalJournal of Electronic Science and Technology
Issue number1
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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