Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis

Gene expression profiling using DNA microarray technique has been shown as a promising tool to improve the diagnosis and treatment of cancer. Recently, many computational methods have been used to discover maker genes, make class prediction and class discovery based on gene expression data of cancer tissue. However, those techniques fall short on some critical areas. These included (a) interpretation of the solution and extracted knowledge. (b) Integrating various sources data and incorporating the prior knowledge into the system. (c) Giving a global understanding of biological complex systems by a complete knowledge discovery framework. This paper proposes a multiple-kernel SVM based data mining system. Multiple tasks, including feature selection, data fusion, class prediction, decision rule extraction, associated rule extraction and subclass discovery, are incorporated in an integrated framework. ALL-AML Leukemia dataset is used to demonstrate the performance of this system.     

Knowledge discovery in databases using lattices

The rapid pace at which data gathering, storage and distribution technologies are developing is outpacing our advances in techniques for helping humans to analyse, understand, and digest the vast amounts of resulting data. This has led to the birth of knowledge discovery in databases (KDD) and data mining—a process that has the goal to selectively extract knowledge from data. A range of techniques, including neural networks, rule-based systems, case-based reasoning, machine learning, statistics, etc. can be applied to the problem. We discuss the use of concept lattices, to determine dependences in the data mining process. We first define concept lattices, after which we show how they represent knowledge and how they are formed from raw data. Finally, we show how the lattice-based technique addresses different processes in KDD, especially visualization and navigation of discovered knowledge.     

A decision support system for Evidence Based Medicine

We present a decision support system to let medical doctors analyze important clinical data, like patients medical history, diagnosis, or therapy, in order to detect common patterns of knowledge useful in the diagnosis process. The underlying approach mainly exploits case-based reasoning (CBR), which is useful to extract knowledge from previously experienced cases. In particular, we used sequence data mining to detect common patterns in patients histories and to highlight the effects of medical practices, based on evidence.We also exploited data warehousing techniques, such OLAP queries to let medical doctor analyze diagnosis along several measures, and recent visual data integration approaches and tools to effectively support the complex task of integrating and reconciling data from different medical data sources. In addition, due to massive presence of textual information within the clinical records of many hospitals, text mining techniques have been devised. In particular, we performed lexical analysis of free text in order to extract discriminatory terms and to derive encoded information. Finally, the system provides user friendly mechanisms to manage the protection of confidential medical data.System validation has been performed, mainly focusing on usability issues, by running experiments based on a large database from a primary public hospital.     

基于动态数据流挖掘的案例推理及其应用

知识的获取、知识库的更新是案例推理技术的应用瓶颈,而许多案例推理系统中的知识库都是静态不变的,满足不了实际问题变化的需要。首先阐述了相关概念,接着提出了一种基于动态数据流挖掘的案例推理模型,其中动态数据流挖掘算法采用改进的数据流聚类算法。通过此模型使用基于动态数据流挖掘的案例推理技术,对数据进行实时挖掘,产生连续、动态的临时案例库,实现知识库的实时更新,从而满足实际问题变化的需要。最后通过该模型在实际中的应用说明其有效性。     

Business data mining — a machine learning perspective

The objective of this paper is to inform the information systems (IS) manager and business analyst about the role of machine learning techniques in business data mining. Data mining is a fast growing application area in business. Machine learning techniques are used for data analysis and pattern discovery and thus can play a key role in the development of data mining applications. Understanding the strengths and weaknesses of these techniques in the context of business is useful in selecting an appropriate method for a specific application. The paper, therefore, provides an overview of machine learning techniques and discusses their strengths and weaknesses in the context of mining business data. A survey of data mining applications in business is provided to investigate the use of learning techniques. Rule induction (RI) was found to be most popular, followed by neural networks (NNs) and case-based reasoning (CBR). Most applications were found in financial areas, where prediction of the future was a dominant task category.     

基于知识支持的建筑施工质量控制系统研究

将知识管理的理念应用到施工质量控制中,加速施工质量控制中知识的循环过程,提高工程质量预测控制和事故诊断能力。通过湖北省建筑工程质量控制研究实践,开发了集建筑质量控制知识管理和决策支持功能于一体的施工质量控制和事故处理系统(CQIS),系统将正向规则推理和基于案例的推理(CBR)相结合,通过RBR接口调用RBR系统,提高案例推理质量诊断的能力,实现基于知识的综合推理功能。该系统已经获得科技成果鉴定和湖北省科技推广成果。     

Recommender system for software project planning one application of revised CBR algorithm

Project management is an experience-driven and knowledge-centralized activity. Therefore, project managers require some assistance to reduce the uncertainty at the early stage of constructing project plans. To overcome the predicament faced by project managers, this investigation proposes a hierarchical criteria architecture (HCA) to enable project managers to describe project requirements adequately. Furthermore, to solve HCA problems, a revised case-based reasoning (RCBR) algorithm, is presented and a recommender system for software project planning is implemented, based on multiple objectives decision techniques and the mining approach. Finally, the proposed RCBR algorithm is successfully applied to analyze 41 real projects from a software consultancy in Taiwan. Experimental results demonstrate that RCBR can efficiently provide related information to help project managers to construct project plans at an early stage. Additionally, the knowledge discovery process of RCBR provides project managers with results similar to what-if analysis. The knowledge can enable project managers to obtain feasible information to re-schedule project resources, and bargain with their customers in the early project planning stage.     

利用数据挖掘技术构造范例库的新方法探究

范例推理技术作为基于规则推理技术的补充,其关键就是能很好地解决知识获取的瓶颈问题,但在范例推理技术的实际应用中,如何高效建立范例库也是一个棘手的问题。采用数据挖掘技术,提出一种综合算法从传统数据库中构造范例库,可望部分解决范例获取的自动化问题,提高系统的运行效率及整体性能。     

基于数据挖掘和范例推理的智能分析决策支持技术综述

介绍了数据挖掘中的一些关键技术、人工智能基于范例推理、决策支持的主要理论及其发展,提出了范例推理、类比学习、规则推理之间的联系,详细探讨了数据挖掘技术、基于范例推理和决策支持理论集成的问题,最后对上述技术在预测领域的综合应用前景作了探讨。     

Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

The threats to people’s health from chronic diseases are always exist and increasing gradually. How to decrease these threats is an important issue in medical treatment. Thus, this paper suggests a model of a chronic diseases prognosis and diagnosis system integrating data mining (DM) and case-based reasoning (CBR). The main processes of the system include: (1) adopting data mining techniques to discover the implicit meaningful rules from health examination data, (2) using the extracted rules for the specific chronic diseases prognosis, (3) employing CBR to support the chronic diseases diagnosis and treatments, and (4) expanding these processes to work within a system for the convenience of chronic diseases knowledge creating, organizing, refining, and sharing. The experiment data are collected from a professional health examination center, MJ health screening center, and implemented through the system for analysis. The findings are considered as helpful references for doctors and patients in chronic diseases treatments.     

gene-CBR: A CASE-BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. This paper presents gene -CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case-based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty-three adult patients with cancer plus a group of six cases corresponding to healthy persons.     

Guest Editors' Introduction: Mining Actionable Knowledge on the Web

The Web-its resources and users-offers a wealth of information for data mining and knowledge discovery. Up to now, a great deal of work has been done applying data mining and machine learning methods to discover novel and useful knowledge on the Web. However, many techniques aim only at extracting knowledge for human users to view and use. Recently, more and more work addresses Web for knowledge that computer systems will use. You can apply such actionable knowledge back to the Web for measurable performance improvements. This special issue of IEEE Intelligent Systems features five articles that address the problem of actionable Web mining.     

LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge-based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute-oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine-learning paradigm, especially learning-from-examples techniques, with rough set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.     

Developing an energy-saving and case-based reasoning information agent with Web service and ontology techniques

Web service and ontology techniques are presented herein for supporting an energy-saving and case-based reasoning information agent. The proposed system is the first energy-saving and case-based reasoning information agent with Web service and ontology techniques in a cloud environment; the proposed architecture is also the first multi-agent structure of an energy-saving information system in a practical environment. Not only can it explore related technologies to establish a Web service platform, but it can also study how to construct cloud interactive diagrams to employ Web service techniques for extensively and seamlessly integrating energy-saving and a case-based reasoning information agent on the Internet. The complete in depth system development, display, and corresponding experiments and comparisons show that the research results not only attest to the feasibility of the proposed architecture, but are also highly successful; on average, 40% of the data queries can be answered by the proposed system, and its rate of correct data solutions is around 85.1%, leaving about 60% of the queries for the backend system to take care of, which can effectively alleviate the overloading problem usually associated with a backend server. Finally, the system is put into a practical environment; after 8 months of experiments, the total energy-saving is 22.44%.     

Development of medical decision support system for leukemia management

A prototype Medical Decision Support System (MDSS) for leukemia patients was developed with emphasis on total management approach from patient registration to diagnosis and treatment. Thus, the MDSS consists of four modules: registry, knowledge model, simulator, and Computer-Assisted Instruction (CAI). Integration of each module improves overall patient management capability and knowledge acquisition capability of the system. Four different knowledge models were developed to predict diagnosis: rule-based reasoning, case-based reasoning, neural network, and discriminant analysis. Among the four, rule-based reasoning produced the most accurate prediction in diagnosis. In the future, the method of leukemia registry can further be extended to the hospital-based cancer registry for other types of cancer. In order to be more effective, the registry should also be integrated with the hospital information system for an easier data entry.     

An Overview of Data Mining and Knowledge Discovery

With massive amounts of data stored in databases,mining information and knowledge in databases has become an important issue in recent research.Researchers in many different fields have shown great interest in date mining and knowledge discovery in databases.Several emerging applications in information providing services,such as data warehousing and on-line services over the Internet,also call for various data mining and knowledge discovery tchniques to understand used behavior better,to improve the service provided,and to increase the business opportunities.In response to such a demand,this article is to provide a comprehensive survey on the data mining and knowledge discorvery techniques developed recently,and introduce some real application systems as well.In conclusion,this article also lists some problems and challenges for further research.     

Identifying biomarker candidates of influenza infection based on scalable time‐course big data of gene expression

Large‐scale biological data processing is an important topic in computational biology, which gives the promotion of many biomedical research. For example, a huge volume of gene expression data can help us achieve potentially useful medical knowledge and identify disease biomarker candidates. Nevertheless, a great deal of attention has been paid to unscalable single‐time‐point expression data after disease symptoms appear (outbreak period) and there have been few investigations into scalable time‐course expression data before disease symptoms appear (incubation) for each sample. By exploiting such dynamic big data in the incubation, we can easily catch early signals of disease states and prevent illness in the first place. In this study, we apply a new mathematical model on biological data of given incubations and identify biomarker candidates using an intellectualized method. The model narrows a large number of alternative genes into a few ones (top genes), which facilitate the discovery of genes related to disease. The aim of our work is to propose a powerful biomarker‐detecting tool, which helps people to aid early diagnosis or identify effective drug targets before the appearance of clinical symptoms.     

Computer aided diagnosis with case-based reasoning and genetic algorithms

This article addresses breast cancer diagnosis using mammographic images. Throughout, the diagnosis is done using the mammographic microcalcifications. The aim of the work presented here is twofold. First, we introduce a back-end phase, based on machine learning techniques, in a previous computer aided diagnosis system. The two machine learning techniques incorporated are case-based reasoning and genetic algorithms. These algorithms look for improving the results obtained by human experts and the previous statistical model. On the other hand, we analyse the obtained results comparing them with the ones provided by other well-known machine learning techniques. The breast cancer dataset used in the experiments come from Girona Health Area. This database contains 216 images previously diagnosed by surgical biopsy.