Hybrid immunizing solution for job recommender system

Two traditional recommendation techniques, content-based and collaborative filtering (CF), have been widely used in a broad range of domain areas. Both methods have their advantages and disadvantages, and some of the defects can be resolved by integrating both techniques in a hybrid model to improve the quality of the recommendation. In this article, we will present a problem-oriented approach to design a hybrid immunizing solution for job recommendation problem from applicant’s perspective. The proposed approach aims to recommend the best chances of opening jobs to the applicant who searches for job. It combines the artificial immune system (AIS), which has a powerful exploration capability in polynomial time, with the collaborative filtering, which can exploit the neighbors’ interests. We will discuss the design issues, as well as the hybridization process that should be applied to the problem. Finally, experimental studies are conducted and the results show the importance of our approach for solving the job recommendation problem.     

基于人工免疫系统的数据挖掘技术原理与应用

该文首先对人工免疫系统的发展历史和自然免疫系统机制进行简要介绍,之后重点对人工免疫系统在数据挖掘领域中的原理与应用研究进行详细分析综述。主要分两个部分,第一部分是从数据挖掘的主要任务——聚类和分类角度阐述人工免疫系统应用现状,第二部分主要从数据挖掘对象子领域——网络数据挖掘和文件挖掘角度分析人工免疫系统的应用,同时对有代表性的方法及其改进过程进行了详细介绍,指出人工免疫数据挖掘技术中的优点和缺点。最后提出新的研究方向。     

人工免疫系统:原理、模型、分析及展望

目前，受生物免疫系统启发而产生的人工免疫系统（Artificial ImmuneSystem,AIS)正在兴起，它作为计算智能研究的新领域，提供了一种强大的信息处理和问题求解范式，该文侧重以AIS的基本原理框架为线索，对其研究状况加以系统综述，首先从AIS的生物原型入手，归纳提炼出其仿生机理，主要包括免疫识别，免疫学习，免疫记忆，克隆选择，个体多样性，分布式和自适应等，进而对几种典型的AIS模型和算法分门别类地进行了细致讨论，随后介绍了AIS在若干具有代表性的领域中的应用情况，最后通过对AIS的特性和存在问题的分析，展望了今后的研究重点和发展趋势。     

人工免疫系统在数据挖掘中的应用

对自然免疫系统机制、人工免疫系统及数据挖掘进行简要介绍。对人工免疫系统在数据挖掘领域中的应用进行详细分析综述。主要阐述人工免疫系统在分类规则、聚类规则等中的应用现状,并对其方法进行详细分析并指出其优缺点。     

Artificial immune systems as a novel soft computing paradigm

Artificial immune systems (AIS) can be defined as computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. Their development and application domains follow those of soft computing paradigms such as artificial neural networks (ANN), evolutionary algorithms (EA) and fuzzy systems (FS). Despite some isolated efforts, the field of AIS still lacks an adequate framework for design, interpretation and application. This paper proposes one such framework, discusses the suitability of AIS as a novel soft computing paradigm and reviews those works from the literature that integrate AIS with other approaches, focusing ANN, EA and FS. Similarities and differences between AIS and each of the other approaches are outlined. New trends on how to create hybrids of these paradigms and what could be the benefits of this hybridization are also presented. Leandro N. de Castro would like to thank the Computing Laboratory and CNPq (Profix n. 540396/01-0) for the financial support and Prof. Dr. Fernando J. Von Zuben for his indispensable comments on the development of a framework for the AIS. Jon Timmis would like to thank the Computing Laboratory, UKC for their continued support in this new area of research.     

人工免疫数据挖掘方法的分析与研究展望

目前,受生物免疫系统启发而产生的人工免疫系统正在兴起,它作为计算智能研究的新领域,提供了一种强大的信息处理和问题求解范式,简要介绍了生物免疫系统的结构和相关机理。对人工免疫系统在数据挖掘方面的应用进行了回顾,分析了近年来AIS在数据挖掘应用领域的研究成果,指出了进一步的研究方向。     

Artificial immune systems—today and tomorrow

In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number of challenges to the AIS community that can be undertaken to help move the area forward.

一种基于改进的DBSCAN的面向海量船舶位置数据码头挖掘算法

目前我国正在大力推行"一带一路"航海战略,航海事业蓬勃发展,大量新码头正在修建中。如何快速、准确地更新码头的空间信息,对于分析进出口贸易、提高码头服务效率等具有很强的现实意义。当前我国主要通过人工测绘手段更新海图,更新间隔在3~12月,远不能满足需求。而利用包括国际海事卫星C系统、北斗卫星、Argos卫星等手段获取的船舶位置数据来进行码头挖掘,为解决获得码头空间信息问题提供了新手段。利用自动识别系统AIS获取的海量船舶位置数据,提出了一种基于自优化参数的码头挖掘算法DBSCAN。一方面能够面向不同船舶类型的不同密度分布进行自动学习优化DBSCAN核心参数,进而聚类出包含码头的停泊区域,具备很强的灵活性;另一方面,融合岸基结构物等空间数据,对停泊区域中的锚区和临时停泊区域等进行排除,获取码头的空间信息,并且达到很高的准确率。利用2012年4月至2014年4月两年中国滚装船的真实轨迹数据和国际滚装船真实轨迹数据进行了码头挖掘实验,准确率能够达到93%以上。     

Methods of resource management in problem-oriented computing environment

One of the important classes of computational problems is problem-oriented workflow applications executed in distributed computing environment. A problem-oriented workflow application can be represented by a directed graph whose vertices are tasks and arcs are data flows. For a problem-oriented workflow application, we can get a priori estimates of the task execution time and the amount of data to be transferred between the tasks. A distributed computing environment designed for the execution of such tasks in a certain subject domain is called problem-oriented environment. To efficiently use resources of the distributed computing environment, special scheduling algorithms are applied. Nowadays, a great number of such algorithms have been proposed. Some of them (like the DSC algorithm) take into account specific features of problem-oriented workflow applications. Others (like Min–Min algorithm) take into account many-core structure of nodes of the computational network. However, none of them takes into account both factors. In this paper, a mathematical model of problem-oriented computing environment is constructed, and a new problem-oriented scheduling (POS) algorithm is proposed. The POS algorithm takes into account both specifics of the problem-oriented jobs and multi-core structure of the computing system nodes. Results of computational experiments comparing the POS algorithm with other known scheduling algorithms are presented.     

一个适应性的人工免疫系统的软件构架

适应性是人工免疫系统（AIS，Artificia lImmune System）的重要特性之一。在AIS软件开发应用中，数据源的进化和学习算法的进化是两个有复杂关联的适应性问题。为此我们扩展并改进了已有的AIS构架，提出一个新的适应性软件构架。该构架以基因计算为中心，扩展了元基因来适应数据源的进化，并设计了可接入学习算法构件和算法验证机制来解决算法进化的适应性问题。在该构架支持下，数据源的进化独立于学习算法的设计，同时使学习算法能适用于多种数据源且能独立进化。该构架可简化AIS软件的复杂性，可提高AIS开发应用的效率，也有助于实现将来的自适应的免疫计算。     

特定形式量化关联规则的优化挖掘

量化关联规则的挖掘是数据挖掘的一项重要任务。该文介绍了一种高效的算法,用于挖掘特定形式的量化关联规则。该算法不仅效率高而且很好地解决了区间分隔引起的规则冗余等一系列问题。最后对能够挖掘的规则形式进行了扩展。     

A Survey of artificial immune applications

The artificial immune system (AIS) community has been vibrant and active for a number of years now, producing a prolific amount of research ranging from modeling the natural immune system, to tackling real world applications, using an equally diverse set of immune inspired algorithms. We review the current immune applications of the AIS approach, and propose a number of suggestions to the AIS community that can be undertaken to help move the area forward. Despite many successes of AIS techniques, there remain some open issues which have to be addressed in order to make the AIS a real-world problem solving technique.     

Efficient incremental mining of contrast patterns in changing data

A contrast pattern is a set of items (itemset) whose frequency differs significantly between two classes of data. Such patterns describe distinguishing characteristics between datasets, are meaningful to human experts, have strong discriminating ability and can be used for powerful classifiers. Incrementally mining such patterns is very important for evolving datasets, where transactions can be either inserted or deleted and mining needs to be repeated after changes occur. When the change is small, it is undesirable to carry out mining from scratch. Rather, the set of previously mined contrast patterns should be reused where possible to compute the new patterns. A primary example of evolving data is a data stream, where the data is a sequence of continuously arriving transactions (or itemsets). In this paper, we propose an efficient technique for incrementally mining contrast patterns. Our algorithm particularly aims to avoid redundant computation which might occur due to simultaneous transaction insertion and deletion, as is the case for data streams. In an experimental study using real and synthetic data streams, we show our algorithm can be substantially faster than the previous approach.     

An empirical approach to evaluating dependency locality in hierarchically structured software systems

In software development, especially component-based software development, dependency locality states that relevant software components should be at shorter distances than irrelevant components. This principle is used together with modularity and hierarchy to guide the design of large-scale complex software systems. In previous work, dependency locality and its correlation with design quality were studied by statically measuring the interactions between software components. This paper presents an empirical approach to evaluating the hierarchical structure of software systems through mining their revision history. Two metrics, spatial distance and temporal distance, are adapted to measure the dependencies between software components. The correlation of spatial distance and temporal distance between software components represents a factor that influences system design quality. More specially, a well designed system hierarchy should have a significant positive correlation while a non-significant positive correlation or a negative correlation would signify design flaws. In an application of this approach, we use Mantel test to study the dependency locality of six software systems from Apache projects.     

Reversible privacy preserving data mining: a combination of difference expansion and privacy preserving

Privacy Preserving Data Mining (PPDM) can prevent private data from disclosure in data mining. However, the current PPDM methods damaged the values of original data where knowledge from the mined data cannot be verified from the original data. In this paper, we combine the concept and technique based on the reversible data hiding to propose the reversible privacy preserving data mining scheme in order to solve the irrecoverable problem of PPDM. In the proposed privacy difference expansion (PDE) method, the original data is perturbed and embedded with a fragile watermark to accomplish privacy preserving and data integrity of mined data and to also recover the original data. Experimental tests are performed on classification accuracy, probabilistic information loss, and privacy disclosure risk used to evaluate the efficiency of PDE for privacy preserving and knowledge verification.     

电力调度数据挖掘后处理方法的研究

关联规则是数据依赖关系的有效描述方法，是知识发现研究的重要内容．然而，随着所挖掘数据库规模的增大，由传统数据挖掘算法所生成的大量关联规则常常令用户的使用与分析十分困难．文中提出了一种新方法来解决这个问题并将其运用到电力调度数据挖掘系统中．实验结果表明，该方法消除了大量冗余规则，并且使用户可以从整体上把握整个规则集，提高了关联规则挖掘的准确性和易用性．     

Supporting the strategies to improve elders’ medication compliance by providing ambient aids

In this paper, we present Ambient Information Systems (AIS) that support strategies relevant to enable elders to effectively manage their medication, such as: remind (Remind-Me AIS), guide (GUIDE-Me AIS), and motivate (CARE-Me AIS) them to medicate. We have informed the AIS design through a case study we carried out to understand elders’ deficiencies for adhering to their medication routine. As a result of the case study and the AIS design process; we identified the design issues that should be addressed when developing AIS that cope with the elders needs for living independently. Identifying these design issues is a step toward proposing design guidelines for the development of AIS for elderly. Through a heuristic evaluation, we identified several usability problems that enabled us to improve AIS characteristics, such as the intuitive mapping of the information representations and the visibility of the different AIS states.     

PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data

Association rule mining, originally proposed for market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm PARM with fast support calculation and significant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based Association Rule Mining (PARM) algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data.      

人工免疫在网络故障诊断中的应用研究

人工免疫系统（AIS）已被广泛的应用在许多领域,如数据分析、多峰函数优化、故障检测等。文章将人工免疫方法引入到PMC模型下网络故障诊断中,文中主要研究如何将AIS应用于系统级故障诊断。理论分析和实验结果表明,基于人工免疫系统的网络故障诊断方法在平均和最差情况下均优于传统的方法。