KNOWLEDGE DISCOVERY AND DATA MINING"Kursus ini mengandungi dua bahagian. Pertama membincangkan konsep dan proses penemuan pengetahuan dalam pangkalan data. Ia menekankan penyediaan dan pemprosesan awal data termasuk pembersihan data, integrasi, transformasi, pemilihan data, reduksi dan pendiskretan data. Perlombongan data merupakan satu bahagian utama didlam proses penemuan pengetahuan. Bahagian kedua mebincangkan konsep dan teknik perlombongan data yang melibatkan ekstraksi corak-corak yang mewakili pengetahuan yang disimpan didalam pangkalan data yang besar, gudang data atau sebarang storan penyimpanan maklumat. Ia termasuk prinsip perlombongan data dan fungsinya seperti pengkelasan, petua hubungkait, kluster, teknik-teknik perlombongan data seperti K-kejiranan terdekat, induksi petua, algoritma pohon keputusan, rangkaian neural, algoritma genetik; contoh-contoh perlombongan data. Aplikasi saintifik dan industri juga dibincangkan.
This course consists of two parts. The first part will discuss the concepts and knowledge discovery processes in database. The focus is on data preparation and preprocessing including data cleaning, integration, transformation, data selection, and data reduction and discretion. Data mining is the main part of knowledge discovery process. The second part will discuss the concepts and techniques of data mining that will involve patterns extraction that represents knowledge stored in a big database, data warehouse or any information repository storage. It includes the priciples of data mining and its functions such as classification, relational rules, cluster, data mining techniques for example the nearest K-neighbourhood, induction rules, decision tree algorithm, neural network, genetic algorithm; data mining examples. The scientific and industrial applications also discussed.
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This course consists of two parts. The first part will discuss the concepts and knowledge discovery processes in database. The focus is on data preparation and preprocessing including data cleaning, integration, transformation, data selection, and data reduction and discretion. Data mining is the main part of knowledge discovery process. The second part will discuss the concepts and techniques of data mining that will involve patterns extraction that represents knowledge stored in a big database, data warehouse or any information repository storage. It includes the priciples of data mining and its functions such as classification, relational rules, cluster, data mining techniques for example the nearest K-neighbourhood, induction rules, decision tree algorithm, neural network, genetic algorithm; data mining examples. The scientific and industrial applications also discussed.
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- Lecturer: PROF. MADYA DR. ZALINDA BINTI OTHMAN