ANALISIS DAN IMPLEMENTASI ALGORITMA SQL BASED FREQUENT PATTERN MINING DENGAN FREQUENT PATTERN – GROWTH (FP-GROWTH)

ABSTRAKSI: Scalable data mining dalam database yang berukuran besar saat ini merupakan tantangan pada penelitian database. Integrasi data mining dengan database system merupakan komponen yang sangat penting untuk aplikasi data mining dengan ukuran yang besar.Komponen dasar dalam data mining task adalah mencari frequent pattern dalam sebuah dataset yang diberikan. Kebanyakan pelajaran yang sebelumnya mengadopsi dari Apriori seperti pendekatan candidate set generation and test. Namun, candidate set generation masih mahal, khususnya ketika terdapat database yang berukuran besar.Pada Tugas akhir ini mengimplementasikan dan menyajikan hasil eksperimen dari sebuah SQL based frequent pattern mining dengan sebuah metode frequent pattern growth (FP-growth) baru, yang effisien dan skalabel untuk mencari frequent patterns tanpa candidate generation.Kata Kunci : Data mining, association rule, SQL based frequent pattern mining,ABSTRACT: Scalable data mining in large databases is one of today\u27s real challenges to database research area. The integration of data mining with database systems is an essential component for any successful large scale data mining application.A fundamental component in data mining tasks is finding frequent patterns in a given dataset. Most of the previous studies adopt an Apriori like candidate set generation and test approach. However, candidate set generation is still costly, especially when the database is large.This final project implement and present experimental result of SQL based frequent pattern mining with a novel frequent pattern growth (FP-growth) method, which is efficient and scalable for mining frequent patterns without candidate generation.Keyword: Data mining, association rule, SQL based frequent pattern mining

Frequent-pattern based iterative projected clustering

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.published_or_final_versio

Frequent-pattern based iterative projected clustering

Irrelevant attributes add noise to high dimensional clusters and make traditional clustering techniques inappropriate. Projected clustering algorithms have been proposed to find the clusters in hidden subspaces. We realize the analogy between mining frequent itemsets and discovering the relevant subspace for a given cluster. We propose a methodology for finding projected clusters by mining frequent itemsets and present heuristics that improve its quality. Our techniques are evaluated with synthetic and real data; they are scalable and discover projected clusters accurately. © 2003 IEEE.published_or_final_versio