Teori Data Mining

Data Mining adalah suatu istilah yang digunakan untuk menguraikan penemuan pengetahuan di dalam database. Data mining adalah proses yang menggunakan teknik statistik, matematika, kecerdasan buatan dan machine learning untuk mengekstraksi dan mengidentifikasi informasi yang bermanfaat dan pengetahuan yang terakit dari berbagai database besar (Turban, 2005).
Secara umum data mining memiliki empat tugas utama (Sahu, Shrma & Gondhalakar, 2011):

Klasifikasi (Classification)
Klasifikasi bertujuan untuk mengklasifikasikan item data menjadi satu dari beberapa kelas standar. Sebagai contoh, suatu program email dapat mengklasifikasikan email yang sah dengan email spam. Beberapa algoritma klasifikasi antara lain pohon keputusan, nearest neighbor, nave bayes, neural networks dan support vector machines.

Regresi (Regression)
Regresi merupakan pemodelan dan investigasi hubungan dua atau lebih variabel. Dalam analisis regresi ada satu atau lebih variabel independent atau prediktor yang biasa diwakili dengan notasi X dan satu variabel respon yang biasa diwakili dengan notasi Y (Santosa, 2007).

Pengelompokan (Clustering)
Clustering merupakan metode pengelompokan sejumlah data ke dalam klaster (group) sehingga dalam setiap klaster berisi data yang semirip mungkin. 

Pembelajaran Aturan Asosiasi (Association Rule Learning)
Pembelajaran aturan asosiasi mencari hubungan antara variabel. Sebagai contoh suatu toko mengumpulkan data kebiasaan pelanggan dalam berbelanja. Ketika menggunakan pembelajaran aturan asosiasi, toko tersebut dapat menentuan produk yang sering dibeli bersamaan dan menggunakan informasi ini untuk tujuan pemasaran.

Daftar Pustaka  
Santosa, B., 2007, Data mining Terapan, GrahaIlmu, Surabaya
Turban, E., dkk, 2005, Decicion Support Systems and Intelligent Systems, Andi Offset, Yogyakarta

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