Mining highly correlated least association rules using scalable trie-based algorithm

Development of least association rules (ARs) mining algorithms is one of the more challenging areas in data mining. Exclusive measurements, complexity and excessive computational cost are the main obstacles as compared to frequent pattern mining. Indeed, most previous studies still use the Apriori-like algorithms. To address this issue, this article proposes a new correlation measurement called definite factor (DF) and a scalable trie-based algorithm named significant least pattern growth (SLP-Growth). This algorithm generates the least patterns based on interval support and finally determines it significances using DF. Experiments with the real datasets show that the SLP-Growth can discover highly positive correlated and significant of least ARs. Indeed, it also outperforms the fast frequent pattern-Growth algorithm up to two times, thus verifying its efficiency.     

开采地面沉陷预测的自适应神经模糊推理方法研究

现行各种开采地面沉陷预测方法均存在着一个共同的缺陷,均不能在集成以往开采地面沉陷工程实 例的基础上对某一地下采矿工程所引起的地面沉陷进行预测,而只能根据某种物理的或力学的方法对其进行预 测。人类在工程实践中所创造的开采地面沉陷方面的经验是非常宝贵的财富,应当在建立开采地面沉陷预测方 法时加以充分利用。以所收集的开采地面沉陷工程实例为基础现行各种开采地面沉陷预测方法均存在着一个共同的缺陷,均不能在集成以往开采地面沉陷工程实 例的基础上对某一地下采矿工程所引起的地面沉陷进行预测,而只能根据某种物理的或力学的     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

免疫进化机制及其在时序模式挖掘中的应用研究

针对目前动态数据挖掘中存在的问题,提出基于数据增量的动态挖掘进程概念;在动态挖掘进程和生物免疫进化过程的相似性基础上,提出了知识发现中的免疫进化机制的基本内涵;给出了基于免疫进化机制的时序模式挖掘算法及其实验分析,以验证理论的正确性和有效性。     

规则型数据采掘工具集AMINER

介绍了一个规则型数据采掘工具集ＡＭＩＮＥＲ,其目标是增强知识表达的能力,并形成一个实用的数据采掘工具,使其能适用于多种不同的应用领域。工具集以一定程度上通用的ＣＲＤ方法为基本采掘算法,分为数据预处理、数据采掘和数据评价三大主要模块。系统原型是一个在关系或演绎数据库中采掘规则型知识,并对领域知识和采掘结果进行管理的系统,其主要功能包括数据采掘,领域知识的管理和采掘结果的管理,最后,对未来的工作提出了     

基于数据挖掘技术的输电工程造价估算

基于某地区输电线路的历史工程造价数据,应用相关分析和聚类分析以及支持向量机理论等数据挖掘技术对电力输电工程的建设造价估算进行研究,提出了一种新的输电线路造价估算的预测模型.首先借助SPSS15.0软件采用相关分析对指标属性进行优化精简,构建了输电线路造价估算的技术指标体系,然后通过聚类分析对输电线路历史工程造价数据进行数据清洗及预处理,最后采用了基于支持向量机的回归模型来构建输电工程造价估算的预测模型.通过应用MATLAB7.0软件对实际输电线路工程设计概算数据的仿真分析,证明了该模型的有效性和可行性.     

Optimising product configurations with a data-mining approach

Customers benefit from the ability to select their desired options to configure final products. Manufacturing companies, however, struggle with the dilemma of product diversity and manufacturing complexity. It is important, therefore, for them to capture correlations among the options provided to the customers. In this paper, a data mining approach is applied to manage product diversity and complexity. Rules are extracted from historical sales data and used to form sub-assemblies as well as product configurations. Methods for discovering frequently ordered product sub-assemblies and product configurations from ‘if-then’ rules are discussed separately. The development of the sub-assemblies and configurations allows for effective management of enterprise resources, contributes to the innovative design of new products, and streamlines manufacturing and supply chain processes. The ideas introduced in this paper are illustrated with examples and an industrial case study.     

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN) model is applied for the effective prediction of rainfall data. Moreover, COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning (COBL) with traditional MFO algorithm. Finally, the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model. For demonstrating the improved outcomes of the COMFO-DLRP approach, a sequence of simulations were carried out and the outcomes are assessed under distinct measures. The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.     

关联规则挖掘在企业运作策略调查问卷中的应用

通过分析企业运作策略问卷调查数据,提取出不同的运作策略与绩效间的关联规则。首先介绍了关联规则以及关联规则兴趣度的度量。对于问卷数据,进行了预处理,确定挖掘的数据集及相关属性。然后应用关联规则挖掘工具,挖掘出潜在的有用规则。通过分析关联规则,找出企业运作策略及绩效间的关系,为企业提供决策支持,提高企业的竞争力。     

The Needs and Benefits of Applying Textual Data Mining within the Product Development Process

As a result of the growing competition in recent years, new trends such as increased product complexity, changing customer requirements and shortening development time have emerged within the product development process (PDP). These trends have added more challenges to the already‐difficult task of quality and reliability prediction and improvement. They have given rise to an increase in the number of unexpected events in the PDP. Traditional tools are only partially adequate to cover these unexpected events. As such, new tools are being sought to complement traditional ones. This paper investigates the use of one such tool, textual data mining for the purpose of quality and reliability improvement. The motivation for this paper stems from the need to handle ‘loosely structured textual data’ within the product development process. Thus far, most of the studies on data mining within the PDP have focused on numerical databases. In this paper, the need for the study of textual databases is established. Possible areas within a generic PDP for consumer and professional products, where textual data mining could be employed are highlighted. In addition, successful implementations of textual data mining within two large multi‐national companies are presented. Copyright © 2003 John Wiley & Sons, Ltd.     

面向产品设计迭代的缺陷信息挖掘方法研究

目的基于主题模型与关联算法,研究中文环境下服务于产品设计迭代的缺陷挖掘方法。方法以网络产品社区作为研究对象,利用数据挖掘技术抓取社区用户的贡献内容,归纳面向产品缺陷分析的语法结构与候选词集,使用主题模型分析语料库中所包含的主题(产品属性)数量以及各个主题下的关键词分布,利用关联算法与紧凑规则找出每个主题下的强关联规则,解析后获得产品缺陷信息。结果通过对小米4型智能手机用户贡献内容的实证分析,识别出了该产品用户声量最高的十四个关键问题。结论两组对比实验的结果表明,所提出的方法能较好地识别、定位用户贡献内容中所包含的产品缺陷/不足信息,拥有较高的准确率与召回率。该方法能够为企业开展产品设计创新活动提供必要的支持。     

An intelligent mining system for diagnosing medical images using combined texture‐histogram features

The aim of this article is to design an expert system for medical image diagnosis. We propose a method based on association rule mining combined with classification technique to enhance the diagnosis of medical images. This system classifies the images into two categories namely benign and malignant. In the proposed work, association rules are extracted for the selected features using an algorithm called AprioriTidImage, which is an improved version of Apriori algorithm. Then, a new associative classifier CLASS_Hiconst ( CL assifier based on ASS ociation rules with Hi gh Con fidence and S uppor t ) is modeled and used to diagnose the medical images. The performance of our approach is compared with two different classifiers Fuzzy‐SVM and multilayer back propagation neural network (MLPNN) in terms of classifier efficiency with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The experimental result shows 96% accuracy, 97% sensitivity, and 96% specificity and proves that association rule based classifier is a powerful tool in assisting the diagnosing process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 194–203, 2013     

A Security Sensitive Function Mining Approach Based on Precondition Pattern Analysis

Security-sensitive functions are the basis for building a taint-style vulnerability model. Current approaches for extracting security-sensitive functions either don’t analyze data flow accurately, or not conducting pattern analyzing of conditions, resulting in higher false positive rate or false negative rate, which increased manual confirmation workload. In this paper, we propose a security sensitive function mining approach based on preconditon pattern analyzing. Firstly, we propose an enhanced system dependency graph analysis algorithm for precisely extracting the conditional statements which check the function parameters and conducting statistical analysis of the conditional statements for selecting candidate security sensitive functions of the target program. Then we adopt a precondition pattern mining method based on conditional statements nomalizing and clustering. Functions with fixed precondition patterns are regarded as security-sensitive functions. The experimental results on four popular open source codebases of different scales show that the approach proposed is effective in reducing the false positive rate and false negative rate for detecting security sensitive functions.     

Some Challenges of Deep Mining

An increased global supply of minerals is essential to meet the needs and expectations of a rapidly rising world population. This implies extraction from greater depths. Autonomous mining systems, developed through sustained R&D by equipment suppliers, reduce miner exposure to hostile work environments and increase safety. This places increased focus on “ground control” and on rock mechanics to define the depth to which minerals may be extracted economically. Although significant efforts have been made since the end of World War II to apply mechanics to mine design, there have been both technological and organizational obstacles. Rock in situ is a more complex engineering material than is typically encountered in most other engineering disciplines. Mining engineering has relied heavily on empirical procedures in design for thousands of years. These are no longer adequate to address the challenges of the 21st century, as mines venture to increasingly greater depths. The development of the synthetic rock mass (SRM) in 2008 provides researchers with the ability to analyze the deformational behavior of rock masses that are anisotropic and discontinuous—attributes that were described as the defining characteristics of in situ rock by Leopold Müller, the president and founder of the International Society for Rock Mechanics (ISRM), in 1966. Recent developments in the numerical modeling of large-scale mining operations (e.g., caving) using the SRM reveal unanticipated deformational behavior of the rock. The application of massive parallelization and cloud computational techniques offers major opportunities: for example, to assess uncertainties in numerical predictions; to establish the mechanics basis for the empirical rules now used in rock engineering and their validity for the prediction of rock mass behavior beyond current experience; and to use the discrete element method (DEM) in the optimization of deep mine design. For the first time, mining—and rock engineering—will have its own mechanics-based “laboratory.” This promises to be a major tool in future planning for effective mining at depth. The paper concludes with a discussion of an opportunity to demonstrate the application of DEM and SRM procedures as a laboratory, by back-analysis of mining methods used over the 80-year history of the Mount Lyell Copper Mine in Tasmania.     

Abnormal Event Correlation and Detection Based on Network Big Data Analysis

With the continuous development of network technology, various large-scale cyber-attacks continue to emerge. These attacks pose a severe threat to the security of systems, networks, and data. Therefore, how to mine attack patterns from massive data and detect attacks are urgent problems. In this paper, an approach for attack mining and detection is proposed that performs tasks of alarm correlation, false-positive elimination, attack mining, and attack prediction. Based on the idea of CluStream, the proposed approach implements a flow clustering method and a two-step algorithm that guarantees efficient streaming and clustering. The context of an alarm in the attack chain is analyzed and the LightGBM method is used to perform false-positive recognition with high accuracy. To accelerate the search for the filtered alarm sequence data to mine attack patterns, the PrefixSpan algorithm is also updated in the store strategy. The updated PrefixSpan increases the processing efficiency and achieves a better result than the original one in experiments. With Bayesian theory, the transition probability for the sequence pattern string is calculated and the alarm transition probability table constructed to draw the attack graph. Finally, a long-short-term memory network and embedding word-vector method are used to perform online prediction. Results of numerical experiments show that the method proposed in this paper has a strong practical value for attack detection and prediction.     

Mining citation information from CiteSeer data

The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.     

Strategic eco-design map of the complex products: toward visualisation of the design for environment

With growing sustainability and environmental concerns regarding the products, decision-makers and business managers need to integrate sustainability into business strategies and support it via systematic business processes and decision support tools. With the integration of the eco-efficiency attributes to the product development, more data should be analysed in engineering design. This integration also increases the complexity of the design process due to the large volume of data processing and diversity of the different attributes and features of the products. In this paper, we used the stock market metaphor to develop a visual data mining approach to the strategic eco-design assessment of the complex products. We presented a fresh framework using clustering and visualisation techniques to analyse the eco-efficiency profile of the different modules, components and parts of a complex product, and provide an efficient data exploration tool for decision-makers to facilitate processing of eco-design attributes, and strategic objectives at the same time. An illustrative example is provided to show the procedure of the application and the effectiveness of the proposed approach.     

Rule-based optimization and multicriteria decision support for packaging a truck chassis

Trucks are highly individualized products where exchangeable parts are flexibly combined to suit different customer requirements, this leading to a great complexity in product development. Therefore, an optimization approach based on constraint programming is proposed for automatically packaging parts of a truck chassis by following packaging rules expressed as constraints. A multicriteria decision support system is developed where a database of truck layouts is computed, among which interactive navigation then can be performed. The work has been performed in cooperation with Volvo Group Trucks Technology (GTT), from which specific rules have been used. Several scenarios are described where the methods developed can be successfully applied and lead to less time-consuming manual work, fewer mistakes, and greater flexibility in configuring trucks. A numerical evaluation is also presented showing the efficiency and practical relevance of the methods, which are implemented in a software tool.     

Modified 2 Satisfiability Reverse Analysis Method via Logical PermutationOperator

The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data. Specifically, the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions. The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy. To address the issues, the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizingthedefinitive finite arrangement of attributes. This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network. Based on the theory, the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach. To validate the impact of the logical permutation on the retrieval phase of the logic mining model, the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and time-series datasets. The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility, accuracy, and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.     

基于CBR的中心渔场预报

针对海洋渔业遥感信息与资源评估服务系统中中心渔场预报的问题,提出了一种多策略的基于CBR的趋势预测方法,通过不同的相似性度量方法,复用完全相似或者条件相似的历史序列,利用领域专家规则对预报结果进一步修正,取得了较好的预防结果,相关系统正在应用推广之中。