Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.     

分类挖掘技术在高校人事管理中的应用

探讨分类挖掘技术在高校实际工作中的应用方式与应用领域。以高校人事管理为模型，应用数据挖掘中决策树算法，对高校人力资源数据源中的信息进行分析，发现其中有价值的数据模式，寻找其中存在的关系和规则，对高校人才规划提供比较客观的决策支持。     

基于数据挖掘的OLAP分析技术及应用

数据仓库和数据挖掘技术是目前信息技术研究的热点问题之一,介绍了数据挖掘(data mining)、数据仓库(datawarehouse)及联机分析处理(OLAP)技术。并以零售业决策支持系统为例,介绍了该系统的体系结构、数据仓库的设计、系统实现关键技术及各子系统功能,着重论述了如何利用OLAP技术实现企业决策支持系统。     

USERS' TWO-DIMENSIONAL INTEREST AND ITS STORAGE METHOD

The users' interest can be mined from the web cache and can be used widely. The interest can be specialized by the two-tuple (term, weight) in the simple interest model, in which the association relations are not mined, and then the interest cannot be associated in expressing the users' interest. Based on analyzing the WWW cache model, this letter brings forward a two-dimensional interest model and gives the interrelated methods on how to store the two-dimensional interest model effectively.     

USERS’ TWO-DIMENSIONAL INTEREST AND ITS STORAGE METHOD

The users‘ interest can be mined from the web cache and can be used widely.The interest can be specialized by the two-tuple(term,weight) in the simple interest model,in which the association relations are not mined.and then interest cannot be associated in expressing the users‘interest.Based on analyzing the WWW cache model,this letter brings forward a twodimensional interest model and gives the interrelated methods on how to store the two-dimensional interest model effectively.     

注浆覆岩离层力学机理及其离层发育分类研究

依据覆岩离层生成的力学机理,结合煤层开采程度的不同,将可注浆层位的离层划分为三类.以岩石的应变为指标,确立了覆岩离层可注浆层位岩梁的断裂步距表达式,理论上证明了划分注浆层位的合理性.根据岩石的全应力应变试验,结合现场实际观测数据,对以应变为指标的岩梁断裂步距表达式做了初步验证.结果表明:可注浆层位的分类和断裂步距表达式的确定对注浆减沉工程具有指导意义.     

试题质量评价研究

教学过程的规范化管理是保证教学质量的重要手段。考试是对教和学的效果的检验，是教学过程管理规范化必须要解决的问题，一直受到教学管理部门的重视，采取了多项措施解决有关问题，其中一个重要的手段是采用试题库。在采用试题库时，必须要有高质量的试题作保证。本探讨基于KDD技术的试题质量评价方法和指标，并通过实例来说明其有效性。     

移动式数据挖掘平台模型

本文提出了一种移动式网络海量数据挖掘平台，给出了数据挖掘任务的形式化描述，详细讨论了平台的体系结构、工作原理与流程。该平台克服了传统网络计算模式的缺陷，支持对异质、分散数据源和知识源的挖掘和检索，具有开放性、可伸缩性、灵活性、可扩展性、健壮性。     

数据挖掘技术在有线电视领域的应用

数据挖掘是近年来在数据库技术、统计学、人工智能、神经网络等学科的基础上发展起来的一门交叉学科，数据挖掘技术已经成功地应用于商业领域，随之又迅速地扩展到社会保险、医学、电信等领域，探讨和分析数据挖掘技术在有线电视领域的应用，并给出了系统体系结构和挖掘算法。