Conditional preference mining based on MaxClique

doi:10.11772/j.issn.1001-9081.2017.11.3107

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (11): 3107-3114.DOI: 10.11772/j.issn.1001-9081.2017.11.3107

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Conditional preference mining based on MaxClique

TAN Zheng, LIU JingLei, YU Hang

School of Computer and Control Engineering, Yantai University, Yantai Shandong 264005, China

Received:2017-05-16 Revised:2017-06-07 Online:2017-11-10 Published:2017-11-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572419, 61572418,61403328).

基于最大团的条件偏好挖掘

谭征, 刘惊雷, 余航

烟台大学计算机与控制工程学院, 山东烟台 264005

通讯作者: 刘惊雷
作者简介:谭征(1968-),男,山东乳山人,副教授,硕士,主要研究方向:数据挖掘、自然语言处理;刘惊雷(1970-),男,山西临猗人,副教授,硕士,CCF会员,主要研究方向:人工智能、理论计算机科学;余航(1998-),男,江西上饶人,主要研究方向:数据挖掘、随机化算法。
基金资助:
国家自然科学基金资助项目（61572419，61572418，61403328）。

Abstract

Abstract: In order to solve the problem that conditional constraints (context constraints) for personalized queries in database were not fully considered, a constraint model was proposed where the context i^+≻i-|X means that the user prefers i⁺ than i^- based on the constraint of context X. Association rules mining algorithm based on MaxClique was used to obtain user preferences, and Conditional Preference Mining (CPM) algorithm combined with context obtained preference rules was proposed to obtain user preferences. The experimental results show that the context preference mining model has strong preference expression ability. At the same time, under the different parameters of minimum support, minimum confidence and data scale, the experimental results of preferences mining algorithm of CPM compared with Apriori algorithm and CONTENUM algorithm show that the proposed CPM algorithm can obviously improve the generation efficiency of user preferences.

Key words: MaxClique, association rule, preference database, conditional preference rule, preference mining

摘要： 针对在数据库的个性化查询中条件约束（或上下文约束）没有被充分考虑的问题，首先提出了条件约束模型i⁺≻i^-|X，它表示在上下文X的约束下，相对于i^-，用户更偏好i⁺。在此模型的基础上，采用最大团（MaxClique）关联规则算法挖掘获得用户偏好；随后又提出了条件偏好挖掘（CPM）算法，该算法结合上下文用于挖掘偏好规则，从而得出用户的偏好。实验结果表明，基于CPM算法的偏好挖掘模型具有较强的偏好表达能力，将CPM算法与基于Apriori的算法以及CONTENUM算法进行了实验对比，实验的主要参数为最小支持度、最小可信度、数据规模等，实验结果进一步表明所提出的CPM算法可明显提高用户偏好规则的产生效率。

关键词: 最大团, 关联规则, 偏好数据库, 条件偏好规则, 偏好挖掘

CLC Number:

TP311

TAN Zheng, LIU JingLei, YU Hang. Conditional preference mining based on MaxClique[J]. Journal of Computer Applications, 2017, 37(11): 3107-3114.

谭征, 刘惊雷, 余航. 基于最大团的条件偏好挖掘[J]. 计算机应用, 2017, 37(11): 3107-3114.

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Conditional preference mining based on MaxClique

基于最大团的条件偏好挖掘

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