A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy

Pemerintah Indonesia menggunakan Nilai Ujian Nasional (NUN) digunakan sebagai standar nasional untuk mengukur keberhasilan siswa. Dengan menggunakan NUN sebagai parameter untuk mengukur tingkat keberhasilan siswa akan ada resiko yang sangat besar jika siswa gagal dalam menjalani Ujian Nasional (UN), oleh karena itu dibutuhkan sebuah model untuk melakukan prediksi terhadap NUN siswa sehingga dapat dilakukan usaha pencegahan terhadap gagalnya siswa dalam UN. Pada paper ini diusulkan sebuah metode untuk melakukan prediksi terhadap NUN siswa dengan menggunakan neuro fuzzy. Data yang digunakan dalam penelitian ini adalah nilai rapor siswa, dan nilai IQ siswa. Pada penelitian ini digunakan tiga mata pelajaran sebagai subjek penelitian yaitu Matematika, Bahasa Indonesia dan Bahasa Inggris.Berdasarkan hasil pengujian yang dilakukan menunjukkan bahwa metode neuro fuzzy dengan menggunakan data nilai dan data IQmemberikan akurasi prediksi terbaik pada mata pelajaran matematika sebesar 74%.

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Improving Sales Analysis in Retail Sale using Data Mining Algorithm with Divide and Conquer Method

International Journal of Engineering Research and ◽

10.17577/ijertv7is070098 ◽

2018 ◽

Vol V7 (07) ◽

Author(s):

Myint Myint Yee ◽

Keyword(s):

Data Mining ◽

Divide And Conquer ◽

Data Mining Algorithm ◽

Retail Sale ◽

Mining Algorithm ◽

Using Data

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Finding Active Membership Functions for Genetic-Fuzzy Data Mining

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622015500297 ◽

2015 ◽

Vol 14 (06) ◽

pp. 1215-1242 ◽

Cited By ~ 3

Author(s):

Chun-Hao Chen ◽

Tzung-Pei Hong ◽

Yeong-Chyi Lee ◽

Vincent S. Tseng

Keyword(s):

Data Mining ◽

Association Rules ◽

Fuzzy Data ◽

Membership Functions ◽

Linguistic Terms ◽

Fuzzy Association Rules ◽

Fitness Functions ◽

The Given ◽

Binary Strings ◽

Active Membership

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

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