Data Mining, Evaluation Techniques in

In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed

Market Basket Analysis (MBA) is a crucial technique used in the field of data mining to understand consumer purchasing patterns. The importance of advertisement in an MBA has been widely acknowledged as a key factor in influencing consumer behavior. With the advent of technology, MBA is undergoing a paradigm shift, with new tools and techniques being developed to improve its accuracy and efficiency. This research paper focuses on the various techniques used in MBA, the role of advertisement in MBA, and the impact of technology on the improvement of MBA. The paper discusses the various algorithms and data mining techniques used in MBA and their advantages and disadvantages. Additionally, it analyzes the importance of advertisement in MBA, its impact on consumer behavior, and the role of technology in enhancing MBA. The paper concludes by highlighting the potential of technology in revolutionizing the MBA field, providing more accurate and efficient results, and ultimately improving business outcomes.

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Knowledge discovery from genomic data has become an important research area for biologists. Nowadays, a lot of data is available on the web, but the corresponding knowledge is not necessarly also available. For example, the first draft of the human genome, which contains 3×109 letters, has been achieved in June 2000, but up to now only a small part of the hidden knowledge has been discovered. The aim of bioinformatics is to bring together biology, computer science, mathematics, statistics and information theory to analyze biological data for interpretation and prediction. Hence many problems encountered while studying genomic data may be modeled as data mining tasks, such as feature selection, classification, clustering, and association rule discovery. An important characteristic of genomic applications is the large amount of data to analyze and it is, most of the time, not possible to enumerate all the possibilities. Therefore, we propose to model these knowledge discovery tasks as combinatorial optimization tasks, in order to apply efficient optimization algorithms to extract knowledge from large datasets. To design an efficient optimization algorithm, several aspects have to be considered. The main one is the choice of the type of resolution method according to the characteristics of the problem. Is it an easy problem, for which a polynomial algorithm may be found? If yes, let us design such an algorithm. Unfortunately, most of the time the response to the question is ‘NO’ and only heuristics, that may find good but not necessarily optimal solutions, can be used. In our approach we focus on evolutionary computation, which has already shown an interesting ability to solve highly complex combinatorial problems. In this chapter, we will show the efficiency of such an approach while describing the main steps required to solve data mining problems from genomics with evolutionary algorithms. We will illustrate these steps with a real problem.

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)Robust Unsupervised Feature Selection on Networked DataJundong Li, Xia Hu, Liang Wu, and Huan LiuJundong Li, Xia Hu, Liang Wu, and Huan Liupp.387 - 395Chapter DOI:https://doi.org/10.1137/1.9781611974348.44PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Feature selection has shown its effectiveness to prepare high-dimensional data for many data mining and machine learning tasks. Traditional feature selection algorithms are mainly based on the assumption that data instances are independent and identically distributed. However, this assumption is invalid in networked data since instances are not only associated with high dimensional features but also inherently interconnected with each other. In addition, obtaining label information for networked data is time consuming and labor intensive. Without label information to direct feature selection, it is difficult to assess the feature relevance. In contrast to the scarce label information, link information in networks are abundant and could help select relevant features. However, most networked data has a lot of noisy links, resulting in the feature selection algorithms to be less effective. To address the above mentioned issues, we propose a robust unsupervised feature selection framework NetFS for networked data, which embeds the latent representation learning into feature selection. Therefore, content information is able to help mitigate the negative effects from noisy links in learning latent representations, while good latent representations in turn can contribute to extract more meaningful features. In other words, both phases could cooperate and boost each other. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Previous chapter Next chapter RelatedDetails Published:2016eISBN:978-1-61197-434-8 https://doi.org/10.1137/1.9781611974348Book Series Name:ProceedingsBook Code:PRDT16Book Pages:1-867

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

The subject of the paper is methods of classification of data mining and data processing tasks in the economy, as well as their classification characteristics. Methodology. The research used the general scientific methodology of analysis, synthesis, generalization, comparison. Taxonomy methods are used in the compilation of the classification. The selection of actual economic problems is carried out by researching scientific publications on analysis and data processing. The purpose of the article is to compile an actual classification of data mining and data processing tasks in the economy and to refine the terminology of this branch of science. Results. The classification of data mining tasks is proposed, consisting of four levels. All economic tasks of data mining are divided into two large groups: predictive and descriptive. Each group is subdivided into several classes, which combine tasks with similar taxonomic features. These are the classes of tasks classification, regression, clustering, link analysis, and outlier analysis. Classes of tasks are divided into types. An important criterion for this is the dimensionality of the input data representation. It means the number of neighbours for each individual data element. The data can be presented in the form of series, matrices, and graphs. The methods of analysing each form of data presentation vary considerably. The definition of data processing is clarified and a classification of relevant tasks is proposed. At the top level of classification, the data processing tasks are divided into two groups, depending on whether the order of the elements in the input data changes or not. The main classes of data processing tasks are identified, such as ranking, sorting, filtering, cleansing, recovery, and quantization. Practical use. The results of the research can be used to model intellectual decision-support systems. They allow improving the processes of problem formulation in data mining and data processing tasks. Also, it helps in the formalization of the procedures for selecting methods of their solutions. This leads to an increase in economic efficiency.

Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.

Data mining tasks are often composed by multiple stages that may be linked each other to form various execution flows. Moreover, data mining tasks are often distributed since they involve data and tools located over geographically distributed environments, like the Grid. Therefore, it is fundamental to exploit effective formalisms, such as workflows, to model data mining tasks that are both multi-staged and distributed. The goal of this work is defining a workflow formalism and providing a visual software environment, named DIS3GNO, to design and execute distributed data mining tasks over the Knowledge Grid, a service-oriented framework for distributed data mining on the Grid. DIS3GNO supports all the phases of a distributed data mining task, including composition, execution, and results visualization. The paper provides a description of DIS3GNO, some relevant use cases implemented by it, and a performance evaluation of the system.KeywordsData MiningComputing NodeGrid ResourceExecution PlanData Mining TaskThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Data mining and scoring tool providers require users to use provider-specific ways to invoke their services. The provider-specific approach could be a major factor affecting why data mining tools and applications are not currently as widespread as one might hope. However, SOA can implements the loose coupling between the services provider and services consumer. Using Web service protocol (such as SOAP, WSDL and UDDI), the data mining task can be encapsulated into a standard Web services. Services consumer can dynamically bound different services provider based on Web services, which implements the integration of data mining applications with data mining and scoring tool. The Java Data Mining API (JDM) is the first attempt to create a standard Java API to access data-mining tools from Java applications. A new service discovery model where quality of service is taken as constraints when searching for Web services would give some confidence to the Web service consumers about the quality of the service they are about to invoke.

Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.

With the advent of the era of big data, data has become bigger than ever. Recently, as a fundamental task of pattern recognition, predict and data mining, feature selection has aroused wide public concern. However, extant methods on feature selection have an $O(\left|C\right|^x\left|U\right|^y)$ time complexity, which is the bottleneck preventing people from exploring knowledge in large-scale or high-dimensional datasets. Based on bijective soft sets, we propose a new rationale for feature selection, which can help break that bottleneck. Subsequently, this paper proposes an $O(\left|U\right|)$ feature-selection method whose computational time increases linearly only with the number of instances. To validate the proposed method, we conduct extensive experiments on the University of California Irvine (UCI) datasets in which large-scale and high-dimensional datasets containing four million instances and over three million features are included. The results reveal that the proposed method is an efficient, effective, and outperforms traditional methods in runtime, which can save massive computing resources. Moreover, the proposed method can be applied to feature selection for large-scale and gigantic-dimensional datasets, which are difficult to process with traditional methods.

Decision support systems (DSS) have evolved since 1970s. The functions of the system include database access, data preprocessing, data mining, and data visualization. In this paper, a developing platform model Architecture for OLAP is introduced. The system provides a complete solution for decision making. A data mining task may integrate with several mining algorithms. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. A task manager helps users select data sources and mining algorithms, and build corresponding task models by providing a task wizard. The task processing engine’s function is to schedule and execute tasks.

Mass Estimation, an alternative to density estimation, is proving to be an effective base modeling mechanism in data mining. It is as basic as density estimation which has been the fundamental for most data modeling methods for a wide range of tasks such as classification, clustering and anomaly detection. This paper reviews the theoretical basis of Mass Estimation that can be employed to solve various tasks in data mining and different ways to estimates the mass of data points in different dimensions. The paper also talks about applications of mass estimation in various data mining tasks and their comparison with previously used density estimation technique. The paper reviews the Mass Estimation technique in detail and will be helpful to researchers working in this area, which is relatively new.