應用FP－Tree探勘多層次特徵規則

A Frequent-Pattern Tree Algorithm for Discovering the Multi-level Characteristic Rule

利用資料探勘技術可以幫助企業探勘龐大資料庫中所隱含的資訊以增加競爭優勢，目前資料探勘中應用最廣的技術就是關聯規則，但大部分的演算法皆是在處理單一層次間的關聯規則，只單獨探討商品與商品間的相關性。在本篇研究中，提出了新的應用方式，簡稱為以FP-tree演算法探討多層次特徵規則，主要探討的是以消費者特徵與商品為組合，透過概念階層化架構，配合GID編碼及FP-Growth演算法，進而探勘出消費者特徵與商品間的高度關聯性。

Data mining techniques enable companies to explore meaningful patterns and rules from an enormous database. Association rule mining has been widely used. Many other relevant methods and researches have been published. However, most of the algorithms simply deal with the single level of association rules. In this paper, a new method “The FP-tree Algorithm for Mining the Multi-level Characteristic Rule” is proposed. The purpose of this study is to investigate the highly related association rules among customers’ characteristics and goods. It is expected that the FP-tree algorithm helps the users obtain meaningful association rules and put the data mining techniques into practice.