Commentary: Deep analysis of epistemic frames and passive participants around argumentation and learning in informal learning spaces

Our commentary first discusses three points of interest highlighted by the current studies in terms of breadth of measured behaviors and characteristics, the commensurability of designs, and the importance and challenge of analyzing learning by passive participants. We then discuss how datamining strategies might be organized to support future research building on these points of interest.     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

基于数据仓库的后勤保障决策支持系统的实现

本文在分析了数据仓库技术、联机事件分析处理技术、数据挖掘技术后,提出了综合利用三种技术来解决将管理信息系统（MIS）提高为决策支持系统（DSS）的实现方案并给出了我们的一个实现实例。     

关联规则发现:综述

围绕ＫＤＤ的一个重要发现任务──关联规则发现,介绍了这一领域基本概念、处理技术以及面临的问题。     

Multiple serial episodes matching

Given q+1 strings (a text t of length n and q patterns m₁,…,mq) and a natural number w, the multiple serial episode matching problem consists in finding the number of size w windows of text t which contain patterns m₁,…,mq as subsequences, i.e., for each mi, if mi=p₁,…,pk, the letters p₁,…,pk occur in the window, in the same order as in mi, but not necessarily consecutively (they may be interleaved with other letters). Our main contribution here is an algorithm solving this problem on-line in time O(nq) with an MP-RAM model (which is a RAM model equipped with extra operations).     

数据挖掘在电子商务中的应用研究

本文主要介绍数据挖掘在电子商务中的应用,文章从数据挖掘的定义、数据挖掘的常用方法出发,阐述了数据挖掘在海量信息中提取有效信息的作用,特别是数据挖掘与电子商务的结合可以给人们生活带来便利,给企业带来新的利润空间以及为企业提供决策支持。常用的数据挖掘技术主要包括支持向量机、朴素贝叶斯、聚类、决策树、神经网络和关联规则等,最后介绍了数据挖掘在电子商务的4个主要应用方面,说明了数据挖掘存在的必要性。     

网络数据智能提取

HTML是当前编写网页使用的最多的语言，如何利用HTML，这种标记性语言的特点，让计算机提供给用户快速获取知识的途径成为许多计算机工作者的一大难题。该文介绍一个有效的从互联网上提取知识的方法供参考。把这种方法简单的分成4个途径。①规范化基于HTML的网页脚本；②将网页结构和内容用一棵内容树表示；③比较相似网页的内容树生成提取规则；④将规则和下载程序结合得到最终的知识获取软件。     

电信网络告警中的关联规则改进算法

文章提出了在告警数据库中挖掘关联规则的一种改进算法Apriori-New。     

Development of a Versatile Database for Transport Properties of Foods

The requirement of reliable data on transport properties of foods has emerged in the last decade due to their importance in the design and simulation of food processing operations. An international effort to obtain and analyze the published data has been made recently, due to the lack of reliable theoretical predictive models. In this framework, a user-friendly database system for the storage and maintenance of the transport properties of foodstuffs is proposed and developed in a Microsoft Access environment. The system allows the end-user to input and retrieve data, as well as to view diagrams with single or comparative data and predict values via a fully featured user-friendly interface. Currently the database has registered over 250 articles, which correspond to approximately 1200 records for the thermal conductivity and 1780 records for moisture diffusivity. Due to its flexibility, the system can be used as an universal template for developing similar database systems.     

Inferring contextual preferences using deep encoder-decoder learners

ABSTRACT

Context-aware systems enable the sensing and analysis of user context in order to provide personalised services. Our study is part of growing research efforts examining how high-dimensional data collected from mobile devices can be utilised to infer users’ dynamic preferences that are learned over time. We suggest novel methods for inferring the category of the item liked in a specific contextual situation, by applying encoder-decoder learners (long short-term memory networks and auto encoders) on mobile sensor data. In these approaches, the encoder-decoder learners reduce the dimensionality of the contextual features to a latent representation which is learned over time. Given new contextual sensor data from a user, the latent patterns discovered from each deep learner is used to predict the liked item’s category in the given context. This can greatly enhance a variety of services, such as mobile online advertising and context-aware recommender systems. We demonstrate our contribution with a point of interest (POI) recommender system in which we label contextual situations with the items’ categories. Empirical results utilising a real world data set of contextual situations derived from mobile phones sensors log show a significant improvement (up to 73% improvement) in prediction accuracy compared with state of the art classification methods.