Applying data mining to learn system dynamics in a biological model

Data mining consists of a set of powerful methods that have been successfully applied to many different application domains, including business, engineering, and bioinformatics. In this paper, we propose an innovative approach that uses genetic algorithms to mine a set of temporal behavior data output by a biological system in order to determine the kinetic parameters of the system. Analyzing the behavior of a biological network is a complicated task. In our approach, the machine learning method is integrated with the framework of system dynamics so that its findings are expressed in a form of system dynamics model. An application of the method to the cell division cycle model has shown that the method can discover approximate parametric values of the system and reproduce the input behavior.     

Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

     

Segmentation modeling algorithm: a novel algorithm in data mining

Many enterprises have accumulated a large amount of data over time. To achieve competitive advantages, enterprises need to find effective ways to analyze and understand the vast amounts of raw data they have. Different methods and techniques have been used to reduce the data volume to a manageable level and to help enterprises identify the business value from the data sets. In particular, segmentation methods have been widely used in the area of data mining. In this paper, we present a new algorithm for data segmentation which can be used to build time-dependent customer behavior models. The proposed model has the potential to solve the optimization problem in data segmentation.     

User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.     

Private personalized social recommendations in an IPTV system

In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called “social recommender systems” are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.     

Recent advances in social multimedia big data mining and applications

In the past decade, social media contributes significantly to the arrival of the Big Data era. Big Data has not only provided new solutions for social media mining and applications, but brought about a paradigm shift to many fields of data analytics. This special issue solicits recent related attempts in the multimedia community. We believe that the enclosed papers in this special issue provide a unique opportunity for multidisciplinary works connecting both the social media and big data contexts to multimedia computing.     

A research case study: Difficulties and recommendations when using a textual data mining tool

Although many interesting results have been reported by researchers using numeric data mining methods, there are still questions that need answering before textual data mining tools will be considered generally useful due to the effort needed to learn and use them.     

A hybrid data mining model in analyzing corporate social responsibility

Over the past two decades, corporate social responsibility (CSR) has received worldwide attention. Publication of CSR reports has become the trend for domestic and foreign enterprises. In the constantly changing and competitive corporate environment, public attention has come to be focused on how enterprises play the role of corporate citizen, and how they achieve a balance of profitable, environmental and charitable activities. However, most quantitative CSR studies to date have concentrated on traditional statistical approaches. The data mining technique has not been widely explored in this area. Thus, this investigation proposes a hybrid data mining CSFSC model, which stands for the first letters of CFS, SMOTE, FCM, SVMOAO and C5.0, integrating data-preprocessing approaches, a classification method and a rule generation mechanism for analyzing CSR data. The data-preprocessing approaches include correlation-based feature selection (CFS), the synthetic minority over-sampling technique (SMOTE) and the fuzzy c-means (FCM) clustering algorithm. The support vector machine one-against-one (SVMOAO) method was employed as a classifier for performing multiclassification, and the C5.0 decision tree algorithm was utilized to generate rules from the results of the SVMOAO model. In this study, CSR data collected from China’s listed firms in 2010 were used to test the performance of the proposed model. The empirical results showed that the designed CSFSC model yields satisfactory classification accuracy, and can provide rules for decision makers. Therefore, the presented CSFSC model is a feasible and effective alternative in analyzing CSR data.     

Make it personal: A social explanation system applied to group recommendations

Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system’s output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the system’s group recommendation and explanations about the group’s social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on “tactful” explanations when addressing users’ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users’ intent (likelihood) to follow the recommendations, users’ satisfaction and the system’s efficiency and trustworthiness.     

Rule-based system to detect energy efficiency anomalies in smart buildings,a data mining approach

The rapidly growing world energy use already has concerns over the exhaustion of energy resources and heavy environmental impacts. As a result of these concerns, a trend of green and smart cities has been increasing. To respond to this increasing trend of smart cities with buildings every time more complex, in this paper we have proposed a new method to solve energy inefficiencies detection problem in smart buildings. This solution is based on a rule-based system developed through data mining techniques and applying the knowledge of energy efficiency experts. A set of useful energy efficiency indicators is also proposed to detect anomalies. The data mining system is developed through the knowledge extracted by a full set of building sensors. So, the results of this process provide a set of rules that are used as a part of a decision support system for the optimisation of energy consumption and the detection of anomalies in smart buildings.     

利用Web Services技术构建开放式数据挖掘系统

提出了一种新颖的数据挖掘系统的体系结构,该结构把SOA与传统的挖掘系统结构相结合.在此体系结构的基础上,实现了一个开放式挖掘系统,能够动态集成挖掘算法.将该系统应用于福州地热资源的数据挖掘中,结果证明通过将WebServices技术引入数据挖掘系统的构建中,能大大增强挖掘系统的功能.     

Functional networks as a novel data mining paradigm in forecasting software development efforts

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.     

数据挖掘在入侵检测系统中的应用

入侵检测是近年来出现的网络安全技术,将数据挖掘技术和入侵检测结合是网络安全领域的一个研究课题.介绍了入侵检测系统的基本概念和相关技术,阐述了数据挖掘在入侵检测系统研究中常用的技术,提出了基于数据挖掘的入侵检测系统和一种改进的Apriori算法,并对系统结构及各部分的功能进行了分析.该算法应用于此系统来提取用户行为特征和入侵模式特征,提高了整个系统的性能.     

数据挖掘在供应商评估体系中的应用

数据挖掘是在海量原始数据中发现人们感兴趣的模式的过程，涉及了众多计算机技术。运用数据挖掘技术从工厂企业建立的数据仓库中发现隐藏的知识，对供应商选择和评估。首先要确定评估的指标及其权重。然后是结合制造业流程，利用数据挖掘方法将流程中的数据转化为供应商评估因子，并发现隐含的有用模式，做出预测和潜在供应商的发现，使供应商的选择更加科学合理高效。     

数据挖掘在入侵检测系统中的应用研究

数据挖掘技术在网络安全领域的应用已成为一个研究热点。入侵检测系统是网络安全的重要防护工具,近年来得到广泛的研究与应用,分析了现有入侵检测系统主要检测方法存在的问题,构建了应用数据挖掘技术的入侵检测系统模型以改善入侵检测的精确性和速度。对各种数据挖掘方法对入侵检测系统产生的作用做了描述。     

数据仓库和数据挖掘技术在DSS中的应用研究

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。介绍了数据仓库的特点、体系结构、联机分析处理及数据挖掘技术,讨论了如何在Microsoft SQL Server2000中将数据仓库和数据挖掘技术结合起来开发决策支持系统。     

煤矿系统数据挖掘模型的研究与设计

随着煤矿生产系统信息化、集成化程度的提高,对矿山多源异构数据挖掘系统的研究已成为矿山生产、研究部门共同关注的问题。在分析数据挖掘通用模型以及煤矿数据特点的基础上,以XML作为一种异构数据挖掘的标准,提出了一种煤矿数据挖掘系统原型架构。整个系统模型包括数据获取模块、XML隧道、数据挖掘模块和基于XML的知识表达模块,对各个模块的功能以及XML文档与数据库之间互相转换的关键技术进行了深入的研究探讨。