可拓数据挖掘研究进展

可拓学研究用形式化模型解决矛盾问题的理论与方法,可拓数据挖掘是可拓学和数据挖掘结合的产物,它探讨利用可拓学方法和数据挖掘技术,去挖掘数据库中与可拓变换有关的知识,包括可拓分类知识、传导知识等可拓知识.随着经济全球化的推进,环境的多变促使了信息和知识的更新周期缩短,创新和解决矛盾问题越来越成为各行各业的重要工作.因此,如何挖掘可拓知识就成为数据挖掘研究的重要任务.研究表明,可拓数据挖掘将具有广阔的应用前景.将介绍可拓数据挖掘的集合论基础、基本知识和目前研究的主要内容,并提出今后需要进一步探讨的问题及其发展前景.     

基于可拓集的可拓分类知识获取研究

以可拓集理论为依据,给出基于可拓变换的可拓分类知识的定义,并在信息元集和评价信息元集的基础上,探讨可拓分类知识的获取方法,包括质变域知识的获取、量变域知识的获取和有关拓界的知识的获取.这是可拓数据挖掘的主要内容之一,为从数据库中获取变化的分类知识提供了新的思路.     

解决矛盾问题的可拓模型与可拓知识的研究

可拓学是解决矛盾问题的学科,在可拓学的可拓模型原型的基础上,明确了计算机解决矛盾问题的可拓模型,它包括三部分:关联函数、可拓知识和推理算法,其中关联函数和可拓知识对不同的问题需要利用不同的原理,且它们是逐步变化的.在可拓知识中,关联函数的可拓变换需要通过计算证明其值是逐步增加的.这样,矛盾问题在计算机中才能得到解决.本文通过多个实例来说明解决矛盾问题的可拓模型及可拓知识的建立和实现.     

可拓建筑形态设计变换数据库初探

可拓建筑形态设计变换数据库是可拓学、数据挖掘的理论和方法在建筑形态设计方面的应用.尝试探讨面向建筑形态设计的可拓变换数据库,论述其概念、类型、实例和作用.以理论概述和实例解析的方法,构建了有效的建筑形态设计数据表达方式,提出了建筑形态数据挖掘工作的前提和基础,为可拓建筑形态设计数据挖掘研究拓展了理论基础.     

教师工作量调控策略影响的传导知识挖掘研究

利用可拓数据挖掘技术,将传导知识挖掘方法应用到高校教师工作量管理中.在调控策略主动变换下,计算教师工作量的传导效应及其可信度,获取传导度及传导度区间,挖掘出量变或质变的传导知识.通过某院校的案例研究表明,支持度及可信度较高的传导知识,能帮助院校管理层从量上了解某策略对教师科研及教学工作量产生正面影响或负面影响的程度,以便找出调控教师工作量的合适策略.     

基于传导变换的传导知识研究

基于传导变换的传导知识是一类重要的可拓知识.本文以传导变换和传导效应为基础,规定了传导度和传导知识的定义与类型,为研究传导知识的挖掘奠定理论基础.     

基于物元可拓性的潜信息挖掘

潜信息挖掘是数据挖掘的核心内容 .本文应用可拓论 ,提出了基于物元可拓性的潜信息挖掘方法 ,探讨了潜信息挖掘的发散性方法 ,相关性方法和蕴含性方法 ,这些方法与现有的数据挖掘方法相兼容 ,相互补充 ,相得益彰 .     

可拓策略生成方法与技术研究

知识激活乃至策略生成问题目前仍然是属于探索性阶段.研究了知识生成和智能激发的可拓方法,其核心是利用可拓变换与拓展推理解决矛盾问题,它为技术实现策略生成探索出一条可行之路.     

可拓数据挖掘在高校教学质量评价中的应用

高校在教学和管理工作中积累了大量的数据,但这些数据没有得到有效利用.将可拓数据挖掘技术引入教学领域,从教学评价数据中提取出隐藏在数据之中的有用信息,为教学管理者提供决策支持.首先通过可拓分析,寻找质量达到要求、可进行有效挖掘的教学评价数据,然后对这些数据进行两方面的挖掘:影响教学质量的关键因素挖掘、教学质量与教师特征之间的关联规则挖掘.     

随机事件与随机变量概率分布的拓展研究

给出了随机事元的拓展概率以及随机事元可拓集的概念.运用可拓集合、可拓变换与可拓推理等可拓学的理论与方法,对随机事件发生的概率与随机变量概率分布的变化作了初步的拓展研究.     

Data preparation using data quality matrices for classification mining

Data mining aims to find patterns in organizational databases. However, most techniques in mining do not consider knowledge of the quality of the database. In this work, we show how to incorporate into classification mining recent advances in the data quality field that view a database as the product of an imprecise manufacturing process where the flaws/defects are captured in quality matrices. We develop a general purpose method of incorporating data quality matrices into the data mining classification task. Our work differs from existing data preparation techniques since while other approaches detect and fix errors to ensure consistency with the entire data set our work makes use of the apriori knowledge of how the data is produced/manufactured.     

Synergies between operations research and data mining: The emerging use of multi-objective approaches

Operations research and data mining already have a long-established common history. Indeed, with the growing size of databases and the amount of data available, data mining has become crucial in modern science and industry. Data mining problems raise interesting challenges for several research domains, and in particular for operations research, as very large search spaces of solutions need to be explored. Hence, many operations research methods have been proposed to deal with such challenging problems. But the relationships between these two domains are not limited to these natural applications of operations research approaches. The counterpart is also important to consider, since data mining approaches have also been applied to improve operations research techniques. The aim of this article is to highlight the interplay between these two research disciplines. A particular emphasis will be placed on the emerging theme of applying multi-objective approaches in this context.     

Disparate data fusion for protein phosphorylation prediction

New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method is an extension of current ensemble classification techniques and has the advantage of allowing data to remain in heterogeneous databases. In this paper, we focus on the applicability of such a framework to the protein phosphorylation prediction problem.     

50 years of data mining and OR: upcoming trends and challenges

Data mining involves extracting interesting patterns from data and can be found at the heart of operational research (OR), as its aim is to create and enhance decision support systems. Even in the early days, some data mining approaches relied on traditional OR methods such as linear programming and forecasting, and modern data mining methods are based on a wide variety of OR methods including linear and quadratic optimization, genetic algorithms and concepts based on artificial ant colonies. The use of data mining has rapidly become widespread, with applications in domains ranging from credit risk, marketing, and fraud detection to counter-terrorism. In all of these, data mining is increasingly playing a key role in decision making. Nonetheless, many challenges still need to be tackled, ranging from data quality issues to the problem of how to include domain experts' knowledge, or how to monitor model performance. In this paper, we outline a series of upcoming trends and challenges for data mining and its role within OR.     

Some Basics on Privacy Techniques,Anonymization and their Big Data Challenges

With the progress in the information and communication fields, new opportunities and technologies for statistical analysis, knowledge discovery, data mining, and many other research areas have emerged, together with new challenges for privacy and data protection. Nowadays several personal records are kept in computerized databases. Personal data is collected and kept in census databases, medical databases, employee databases, among others. There has always been an asymmetry between the benefits of computerized databases and the rights of individual data subjects. Some data protection principles can be derived from the legal framework. In this survey, we present some basic cryptographic and non-cryptographic techniques that may be used for enhancing privacy, we focus mainly on anonymization in databases and networks, discuss some differences and interactions among the well-known models of k-anonymity and differential privacy and finally present some challenges to privacy that come from big data analytics.     

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising.     

The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification

In this paper, we present an application of multi-objective metaheuristics to the field of data mining. We introduce the data mining task of nugget discovery (also known as partial classification) and show how the multi-objective metaheuristic algorithm NSGA II can be modified to solve this problem. We also present an alternative algorithm for the same task, the ARAC algorithm, which can find all rules that are best according to some measures of interest subject to certain constraints. The ARAC algorithm provides an excellent basis for comparison with the results of the multi-objective metaheuristic algorithm as it can deliver the Pareto optimal front consisting of all partial classification rules that lie in the upper confidence/coverage border, for databases of limited size. We present the results of experiments with various well-known databases for both algorithms. We also discuss how the two methods can be used complementarily for large databases to deliver a set of best rules according to some predefined criteria, providing a powerful tool for knowledge discovery in databases.