A k‐means clustering‐based security framework for mobile data mining

Data mining is a process of digging data sets from large data‐bases, especially for use to improve the operation efficiency of a company, institution, or an organization. With the increasing cases of cyber and other computer‐related crimes, computer and mobile data security has become a matter of concern, secure, and non‐contaminated data is very important for data mining to improve the performance of a system. This paper proposes a system framework that is able to collect information and then be able to generate alerts in real time. The proposed scheme is then simulated using the K‐means clustering algorithm, which is one of the most popular clustering algorithms to determine the efficiency and the accuracy of the proposed scheme. The paper concludes by proposing further improvements to be undertaken on the proposed system to improve its efficiency and accuracy. Copyright © 2017 John Wiley & Sons, Ltd.     

Web日志挖掘中预处理过程的具体研究

Web日志的数据预处理是Web日志挖掘过程中基础而关键的一步,对之后的模式识别和模式分析有着很大的影响。为了达到有效处理数据的目的,针对此预处理过程中的5个步骤逐一进行分析,并在事务识别这一步骤中,比较了常用的两种算法。最后,基于这些算法思想,在Windows平台下,采用Java语言实现了Web日志预处理。实验结果表明是有效的。     

一种基于多Agent的Web数据挖掘方法

基于多Agent的数据挖掘技术,不仅能够针对不同的Web数据综合采用不同的挖掘算法,而且可以在各站点进行并行挖掘,避免Web通信量过载。在简述Agent技术和Web数据挖掘技术的基础上,结合多Agent和Web数据挖掘,设计出一种新型数据挖掘模型,且进一步阐述了该模型,并做了一些分析测试。结果证明,该方法能有效提高Web数据挖掘的速度、准确率和覆盖率,提高了数据利用率。     

剖分关联规则综述

剖分关联规则是描述剖分面片之间或者面片内空间对象之间的相邻、相连、共生、包含等空间关联的规则。从基本概念、分类、目前研究成果等方面对其进行综述,重点阐述了剖分关联规则的概念、挖掘方法等。通过对现有剖分关联规则研究成果和存在问题的深入剖析,指出了其未来主要的发展方向。     

奇异值分解算法优化

奇异值分解算法在信号处理、图像处理、信息安全等领域均有重要应用.针对该算法存在的性能问题,提出了基于gamma:1驱动的数据重用模型,提高计算负载平衡性,降低数据通信量;给出基于多处理器的并行分解模型,数值试验均表明算法具有较高的并行加速比和效率.     

Mining categorical sequences from data using a hybrid clustering method

The identification of different dynamics in sequential data has become an every day need in scientific fields such as marketing, bioinformatics, finance, or social sciences. Contrary to cross-sectional or static data, this type of observations (also known as stream data, temporal data, longitudinal data or repeated measures) are more challenging as one has to incorporate data dependency in the clustering process. In this research we focus on clustering categorical sequences. The method proposed here combines model-based and heuristic clustering. In the first step, the categorical sequences are transformed by an extension of the hidden Markov model into a probabilistic space, where a symmetric Kullback–Leibler distance can operate. Then, in the second step, using hierarchical clustering on the matrix of distances, the sequences can be clustered. This paper illustrates the enormous potential of this type of hybrid approach using a synthetic data set as well as the well-known Microsoft dataset with website users search patterns and a survey on job career dynamics.     

基于存储库数据挖掘的开源软件成功度量方法

开源软件已广泛应用于各软件领域,例如操作系统、容器等,但目前尚没有一种能够综合度量开源软件的方法.我们在用户兴趣度量和开发参与度量的基础上,提出了一种能够克服度量维度单一的局限性的度量方法.基于DM模型、软件生存力模型等相关文献研究和开源软件存储库数据挖掘,通过对项目过程数据进行聚类、主成分分析、回归分析和对开发过程的思考,本文提出一种基于存储库数据和统计学算法的开源软件成功度度量模型.并通过与用户兴趣度量结果和开发参与度量结果对比分析,证明本文的度量模型能够基于可自动无扰采集的存储库数据,更全面的衡量开源软件项目的成功.度量模型可应用于企业选择优质开源项目、学术研究、智能项目推荐等领域.     

数据挖掘专利综述

尽管科学研究专利是反映科学研究成果的一个重要方面,专利申请本身是一项重要的科学研究工作,但是长期以来,专利所包含的科学研究成果在文献中却没有得到充分的反映.由此,对著名的美国专利和商标委员会数据库(USPATENT & TRADEMARK OFFICE DATABASE)中数据挖掘专利的授权情况进行了分析.对于专利授权比较集中的领域-关联规则、互联网挖掘、聚类算法和并行数据挖掘等方面中的代表性专利进行了总结和分析.最后,指出了当前数据挖掘专利的一些空白领域     

The Method of Virtual Experts in Mathematical Diagnostics

Mathematical Diagnostics (MD) deals with identification problems arising in different practical areas. Some of these problems can be described by mathematical models where it is required to identify points belonging to two or more sets of points. Most of the existing tools provide some identification rule (a classifier) by means of which a given point is assigned (attributed) to one of the given sets. Each classifier can be viewed as a virtual expert. If there exist several classifiers (experts), the problem of evaluation of experts’ conclusions arises. In the paper for the case of supervised classification the method of virtual experts (the VE-method) is described. Based on this method, a generalized VE method is proposed where each of the classifiers can be chosen from a given family of classifiers. As a result, a new optimization problem with a discontinuous functional is stated. Examples illustrating the proposed approach are provided. The work of the second author was supported by the Russian Foundation for Fundamental Studies (RFFI) under Grant No 03-01-00668.     

Gas Forming Processes within Lignite Mining Dumps

Abstract

Gas analyses of the soil atmosphere of lignite mining dumps yielded increased contents of carbon dioxide. To get information about the potential sources and the carbon dioxide releasing capacity of the dumps, samples of dump material were investigated for their contents and isotopic compositions of organic and inorganic carbon as well as the carbon dioxide in the soil atmosphere. The contents of organic and inorganic carbon were found to vary depending on type of dump material. The isotopic composition of the organic carbon ranges between ?24.5 and ?26.5‰, which is typical for humous materials. The carbonates are found to be of marine origin (δ¹³C: +0.5 to ?1.1‰). By means of the isotope investigations it could be shown that the carbon dioxide in the lignite mining dump arises from these two different sources. Mixing ratios can be calculated using the isotope balance equation. Both reaction paths are associated with oxygen consumption and do not result in an increased gas pressure within the dump.