基于遗传算法的有趣模糊规则挖掘算法

针对数据挖掘中多强调分类规则的准确性和可理解性而很少研究规则的有趣性问题。对CHENS和LIUB提出的兴趣规则挖掘方法进行扩展,提出一种基于遗传算法的有趣模糊规则挖掘方法．实验表明该方法是可行的．     

基于SLIQ的数据挖掘分类算法

分类是数据挖掘的一种非常重要的方法。分类的概念是在已有数据的基础上学会一个分类函数或构造出一个分类模型。该函数或模型能够把数据库中的数据记录映射到给定类别中的某一个,从而可以应用于数据预测。大部分数据挖掘工具采用规则发现或决策树分类技术来发现数据模式和规则,其核心是某种归纳算法。这类工具通常是对数据库的数据进行开采,生产规则和决策树,然后对新数据进行分析和预测。本文研究基于SLIQ的数据挖掘分类算法。     

一种基于兴趣度的大型数据库关联规则挖掘方法

数据库关联规则挖掘是数据挖掘研究中一个重要研究课题,但该方法本身存在不足,对于大型数据库,可能产生数以千计的规则,使用户感到无所适从.本文提出对关联规则进行分类的思想,并给出了基于数据统计特性的带兴趣度的关联规则挖掘算法GRMiner和IRMiner,算法实现简单,分析表明该算法是有效的.     

基于Bayes定理的分类规则研究

研究了利用Bayes定理发现分类规则的方法,用Bayes定理可以发现分类规则,然后用分类规则进行数据分类。结合实例针对概念性数据集及包含数值性属性和概念性属性的数据集两种情况进行讨论。通过实例说明Bayes定理是数据挖掘中一种有效的数据分类方法。     

数据挖掘在我国医保方面应用综述

用数据挖掘的方法来研究目前与日俱增的医保数据在我国目前还不是很多,但已具备一定的研究成果。该文对数据挖掘在我国医保领域的应用进行了总结概述,从关联规则发现、数据聚类、分类知识发现、序列模式发现以及其他数据挖掘方法在医保中的应用几方面进行综述,最后对数据挖掘在医保领域的应用做了展望。     

数据挖及其发展研究

讨论了数据挖掘的产生，定义，操作对象和分类方法，论述了数据挖掘可以挖掘的模式及其兴趣度，简要介绍了几种流行的数据挖掘系统，最后提出了挖掘研究今后的若干发展趋势。     

基于贝叶斯理论的分类模式挖掘方法研究

分类规则可以挖掘出某些共同特性，是数据挖掘的重要方法之一。将贝叶斯理论应用于分类模式挖掘算法的设计中，可使分类的错误率最小，设计出更加完善的挖掘算法，从而提高数据挖掘的准确性和有效性。     

数据挖掘中关联规则的有趣性研究

关联规则挖掘是数据挖掘研究中的一个重要方面，而其中一个重要问题是对挖掘出的规则的兴趣度的评估，过去的研究发现，在实际应用中往往很容易从数据源中挖掘出大量的规则，但这些规则中的大部分对用户来说是不感兴趣的，本文对规则的兴趣度度量的两个方面作了讨论：一个是主观兴趣度度量，另一个是客观兴趣度度量，最后介绍了如何利用模板进行挖掘有趣的规则。     

常规挖掘算法在离群数据检测中的应用

数据挖掘以发现常规模式为主体,但离群数据在欺诈分析及安全领域具有重要分析价值,离群数据检测已成为数据挖掘的重要内容。对聚类与分类以及关联规则分析中典型的常规数据挖掘算法如何处理离群数据进行全面分析与总结,讨论了BIRCH、CURE、Chameleon、DBSCAN以及基于共享最近邻的聚类算法以及基于不平衡分类和基于非频繁模式的离群检测技术,给出了一种利用K-最近邻算法的离群数据检测方法,并报告了测试结果。     

利用MLC++实现数据挖掘

数据挖掘是从大量原始数据中抽取隐藏知识的过程。大部分数据挖掘工具采用规则发现和决策树分类技术来发现数据模式和规则,其核心是归纳算法。与传统统计方法相比,基于机器学习技术得到的分类结果具有较好的可解释性。在针对特定的数据集进行数据挖掘时,如果缺乏相应的领域知识,用户或决策者就很难确定选择何种归纳算法。因此,需要尝试各种算法。借助MLC＋＋,决策者能够轻而易举地比较不同分类算法对特定数据集的有效性,从而选择合适的分类算法。同时,系统开发人员也可以利用MLC＋＋设计各种混合算法。     

Mining weighted sequential patterns in a sequence database with a time-interval weight

Sequential pattern mining, including weighted sequential pattern mining, has been attracting much attention since it is one of the essential data mining tasks with broad applications. The weighted sequential pattern mining aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. In the conventional weighted sequential pattern mining, usually pre-assigned weights of data elements are used to get the importance, which are derived from their quantitative information and their importance in real world application domains. In general sequential pattern mining, the generation order of data elements is considered to find sequential patterns. However, their generation times and time-intervals are also important in real world application domains. Therefore, time-interval information of data elements can be helpful in finding more interesting sequential patterns. This paper presents a new framework for finding time-interval weighted sequential (TiWS) patterns in a sequence database and time-interval weighted support (TiW-support) to find the TiWS patterns. In addition, a new method of mining TiWS patterns in a sequence database is also presented. In the proposed framework of TiWS pattern mining, the weight of each sequence in a sequence database is first obtained from the time-intervals of elements in the sequence, and subsequently TiWS patterns are found considering the weight. A series of evaluation results shows that TIWS pattern mining is efficient and helpful in finding more interesting sequential patterns.     

Finding efficiencies in frequent pattern mining from big uncertain data

Many existing data mining algorithms search interesting patterns from transactional databases of precise data. However, there are situations in which data are uncertain. Items in each transaction of these probabilistic databases of uncertain data are usually associated with existential probabilities, which express the likelihood of these items to be present in the transaction. When compared with mining from precise data, the search space for mining from uncertain data is much larger due to the presence of the existential probabilities. This problem is worsened as we are moving to the era of Big data. Furthermore, in many real-life applications, users may be interested in a tiny portion of this large search space for Big data mining. Without providing opportunities for users to express the interesting patterns to be mined, many existing data mining algorithms return numerous patterns—out of which only some are interesting. In this article, we propose an algorithm that allows users to express their interest in terms of constraints, uses the MapReduce model to mine uncertain Big data for frequent patterns that satisfy the user-specified anti-monotone and monotone constraints, as well as balance the load.     

Incremental, online, and merge mining of partial periodic patterns in time-series databases

Mining of periodic patterns in time-series databases is an interesting data mining problem. It can be envisioned as a tool for forecasting and prediction of the future behavior of time-series data. Incremental mining refers to the issue of maintaining the discovered patterns over time in the presence of more items being added into the database. Because of the mostly append only nature of updating time-series data, incremental mining would be very effective and efficient. Several algorithms for incremental mining of partial periodic patterns in time-series databases are proposed and are analyzed empirically. The new algorithms allow for online adaptation of the thresholds in order to produce interactive mining of partial periodic patterns. The storage overhead of the incremental online mining algorithms is analyzed. Results show that the storage overhead for storing the intermediate data structures pays off as the incremental online mining of partial periodic patterns proves to be significantly more efficient than the nonincremental nononline versions. Moreover, a new problem, termed merge mining, is introduced as a generalization of incremental mining. Merge mining can be defined as merging the discovered patterns of two or more databases that are mined independently of each other. An algorithm for merge mining of partial periodic patterns in time-series databases is proposed and analyzed.     

Similarity-Profiled Temporal Association Mining

Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.     

对比模式挖掘研究进展

对比模式挖掘是数据挖掘的一个重要和集中的子领域,主要涉及数据集的模式挖掘和对比处理。它的目的是寻找有趣的对比模式,描述满足各种不同条件的显著差异的数据集。对比的条件可以在类、时间、位置、或其他“维”中定义,当然也可以在他们的组合中定义。对比模式可以代表类之间的不同差异,随时间推移的有趣的变化或者空间趋势变化等等,通过分析两类或多类样本中的对比信息能够得到新的未知信息。对比模式挖掘发展至今,已有了众多的相关技术和算法,在许多领域得到有效应用。本文对现有的对比模式挖掘技术进行了全面的解读,其中包括了它的背景介绍、基本概念、技术算法、相关应用、研究展望等内容,能够为对该方向感兴趣的研究者提供详尽参考。     

Tree pattern mining with tree automata constraints

Most work on pattern mining focuses on simple data structures such as itemsets and sequences of itemsets. However, a lot of recent applications dealing with complex data like chemical compounds, protein structures, XML and Web log databases and social networks, require much more sophisticated data structures such as trees and graphs. In these contexts, interesting patterns involve not only frequent object values (labels) appearing in the graphs (or trees) but also frequent specific topologies found in these structures. Recently, several techniques for tree and graph mining have been proposed in the literature. In this paper, we focus on constraint-based tree pattern mining. We propose to use tree automata as a mechanism to specify user constraints over tree patterns. We present the algorithm CoBMiner which allows user constraints specified by a tree automata to be incorporated in the mining process. An extensive set of experiments executed over synthetic and real data (XML documents and Web usage logs) allows us to conclude that incorporating constraints during the mining process is far more effective than filtering the interesting patterns after the mining process.     

一种有趣关联模式挖掘方法

同一关联挖掘算法算法在不同性质的数据上会表现出不同的性能。针对该问题,提出一种有趣关联模式挖掘方法。介绍模式的兴趣度度量,引入兴趣度预处理过程,并将数据分为2种类型,分别采用不同的算法对这2类数据集进行挖掘。实例表明,该方法能有效提高输出模式的质量。     

Mining unexpected patterns using decision trees and interestingness measures: a case study of endometriosis

Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research.     

Proportional fault-tolerant data mining with applications to bioinformatics

The mining of frequent patterns in databases has been studied for several years, but few reports have discussed for fault-tolerant (FT) pattern mining. FT data mining is more suitable for extracting interesting information from real-world data that may be polluted by noise. In particular, the increasing amount of today’s biological databases requires such a data mining technique to mine important data, e.g., motifs. In this paper, we propose the concept of proportional FT mining of frequent patterns. The number of tolerable faults in a proportional FT pattern is proportional to the length of the pattern. Two algorithms are designed for solving this problem. The first algorithm, named FT-BottomUp, applies an FT-Apriori heuristic and finds all FT patterns with any number of faults. The second algorithm, FT-LevelWise, divides all FT patterns into several groups according to the number of tolerable faults, and mines the content patterns of each group in turn. By applying our algorithm on real data, two reported epitopes of spike proteins of SARS-CoV can be found in our resulting itemset and the proportional FT data mining is better than the fixed FT data mining for this application.     

一个数据挖掘工具的设计与实现

数据挖掘是一个利用各种分析工具在海量数据中发现模型和数据间关系的过程，这些模型和关系可以用来做出预测，该文介绍了一人数据挖掘工具的设计，以Apriori算法为核心，实现了数据挖掘中基于数据库的几种常用挖掘方法，包括基于关系数据库的数据挖掘，不完整数据库中的数据挖掘和根据兴趣度测量来挖掘感兴趣知识的异常关联规则挖掘。