Data Mining Projects Research Articles

Super Computer Heterogeneous Classifier Meta-Ensembles

This article describes the entry of the Super Computer Data Mining (SCDM) Project to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition. The SCDM project is developing data mining tools for parallel execution on Linux clusters. The code is freely available; please contact the first author for a copy. We combine several classifiers, some of them ensemble techniques, into a heterogeneous meta-ensemble, to produce a probability estimate for each test case. We then use a simple decision theoretic framework to form a classification. The meta-ensemble contains a Bayesian neural network, a learning classifier system (LCS), attribute selection based-ensemble algorithms (Filtered At-tribute Subspace based Bagging with Injected Randomness [FASBIR]), and more well-known classifiers such as logistic regression, Naive Bayes (NB), and C4.5.

Privacy-preserving SVM classification

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data, either at a centralized location or in federated form. Increasingly, privacy and security concerns restrict this access, thus derailing data mining projects. What is required is distributed knowledge discovery that is sensitive to this problem. The key is to obtain valid results, while providing guarantees on the nondisclosure of data. Support vector machine classification is one of the most widely used classification methodologies in data mining and machine learning. It is based on solid theoretical foundations and has wide practical application. This paper proposes a privacy-preserving solution for support vector machine (SVM) classification, PP-SVM for short. Our solution constructs the global SVM classification model from data distributed at multiple parties, without disclosing the data of each party to others. Solutions are sketched out for data that is vertically, horizontally, or even arbitrarily partitioned. We quantify the security and efficiency of the proposed method, and highlight future challenges.

ORGANIZATIONAL DATA MINING IN KOREA

This study investigates organizational data mining in Korea. A survey instrument was developed based on a review of the organizational data mining literature and e-mailed to 600 professionals in the field of data mining or data analysis. 90 usable responses were analyzed using stepwise regression. Data quality, process documentation, understanding of data, data integration, data mining outsourcing strategy, understanding of organizational data mining and end-users requests were found to be related to organizational data mining project outcomes.

Mining science data

The data from Scientific simulations, observations, and experiments are now being measured in terabytes and will soon reach the petabyte regime. The size of the data, as well as its complexity, make it difficult to find useful information in the data. This is of course disconcerting to scientists who wonder about the science still undiscovered in the data. The Sapphire Scientific data mining project is addressing this concern by applying data mining techniques to problems ranging in size from a few megabytes to a hundred terabytes in a variety of domains. In this paper, we briefly describe our work in several applications, including the identification of key features for edge harmonic oscillations in the DIII-D tokamak, classification of orbits in a Poincaré plot, and tracking of features of interest in experimental images.

EVALUATION OF CLASSIFIERS FOR AN UNEVEN CLASS DISTRIBUTION PROBLEM

Classification problems with uneven class distributions present several difficulties during the training as well as during the evaluation process of classifiers. A classification problem with such characteristics has resulted from a data mining project where the objective was to predict customer insolvency. Using the data set from the customer insolvency problem, we study several alternative methodologies, which have been reported to better suit the specific characteristics of this type of problem. Three different but equally important directions are examined: (a) the performance measures that should be used for problems in this domain; (b) the class distributions that should be used for the training data sets; and (c) the classification algorithms to be used. The final evaluation of the resulting classifiers is based on a study of the economic impact of classification results. This study concludes to a framework that provides the “best” classifiers, identifies the performance measures that should be used as the decision criterion, and suggests the “best” class distribution based on the value of the relative gain from correct classification in the positive class. This framework has been applied in the customer insolvency problem, but it is claimed that it can be applied to many similar problems with uneven class distributions that almost always require a multi-objective evaluation process.

Mining California vital statistical data

Vital statistics data offer a fertile ground for data mining. In this paper, we discuss the results of a data-mining project on the causes of death aspect of the vital statistics data in the state of California. A data-mining tool called Cubist is used to build predictive models out of two million cases over a nine-year period. The objective of our study is to discover knowledge (trends, correlations or patterns) that may not be gleaned through standard techniques. The generated predictive models allow pertinent state agencies to gain insight into various aspects of the death rates in the state of California, to predict health issues related to the causes of death, to offer an aid to decision or policy-making process and to provide useful information services to the customers. The results obtained in our study contain valuable new information.

Collaborative Data-Mining in an Adolescent Mental Health Service

Summary As full participants in a collaborative clinical data-mining project intended to promote staff reflection and to improve services, clinicians discovered just how valuable their practice expertise was in maintaining project relevance. In this paper, they describe challenges they faced as non-experienced researchers and writers and how their practice skills enabled them to overcome these challenges. Benefits derived include: enhanced skills (in both practice and research), sensitization to previously overlooked areas of practice, and an increased sense of professionalism.

Privacy-preserving clustering with distributed EM mixture modeling

Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.

Privacy-preserving clustering with distributed EM mixture modeling

Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixt...

An enterprise modeling and integration framework based on knowledge discovery and data mining

This paper deals with the conceptual design and development of an enterprise modeling and integration framework using knowledge discovery and data mining. First, the paper briefly presents the background and current state-of-the-art of knowledge discovery in databases and data mining systems and projects. Next, enterprise knowledge engineering is dealt with. The paper suggests a novel approach of utilizing existing enterprise reference architectures, integration and modeling frameworks by the introduction of new enterprise views such as mining and knowledge views. An extension and a generic exploration of the information view that already exists within some enterprise models are also proposed. The Zachman Framework for Enterprise Architecture is also outlined versus the existing architectures and the proposed enterprise framework. The main contribution of this paper is the identification and definition of a common knowledge enterprise model which represents an original combination between the previous projects on enterprise architectures and the Object Management Group (OMG) models and standards. The identified common knowledge enterprise model has therefore been designed using the OMG's Model-Driven Architecture (MDA) and Common Warehouse MetaModel (CWM), and it also follows the RM-ODP (ISO/OSI). It has been partially implemented in Java™, Enterprise JavaBeans (EJB) and Corba/IDL. Finally, the advantages and limitations of the proposed enterprise model are outlined.

A Data Mining Approach for Retailing Bank Customer Attrition Analysis

Deregulation within the financial service industries and the widespread acceptance of new technologies is increasing competition in the finance marketplace. Central to the business strategy of every financial service company is the ability to retain existing customers and reach new prospective customers. Data mining is adopted to play an important role in these efforts. In this paper, we present a data mining approach for analyzing retailing bank customer attrition. We discuss the challenging issues such as highly skewed data, time series data unrolling, leaker field detection etc, and the procedure of a data mining project for the attrition analysis for retailing bank customers. We use lift as a proper measure for attrition analysis and compare the lift of data mining models of decision tree, boosted naive Bayesian network, selective Bayesian network, neural network and the ensemble of classifiers of the above methods. Some interesting findings are reported. Our research work demonstrates the effectiveness and efficiency of data mining in attrition analysis for retailing bank.

A documentation infrastructure for the management of data mining projects

Effective project management is a key factor for successful Knowledge Discovery in Databases (KDD) projects. The systematic documentation of previous knowledge, experiments, data and results is a helpful means of keeping track of the project current status. Despite its value, documentation is most often perceived as an overhead. We propose a documentation infrastructure composed of a documentation model and a supporting environment that allows the capture, storage and retrieval of KDD process-related information and artifacts. The paper describes this infrastructure, and reports preliminary experiences on its use. Preliminary results reveal generalized satisfaction with regard to infrastructure expressiveness and functionality, and highlight the contributions of the documentation produced for improving project management, project execution and team communication. The role of documentation in learning and reuse was also identified.

A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps

The Self-Organizing Map (SOM) is a powerful tool for exploratory data analysis which has been employed in a wide range of data mining applications. We present a novel approach to reveal the inherent hierarchical structure of data using multiple SOMs together with heuristics which optimize the stability. In particular, we address shortcomings of the Growing Hierarchical Self-Organizing Map (GHSOM) regarding the decision which areas in the hierarchical structure need to be represented by a finer granularity and which areas do not. We introduce the Tension and Mapping Ratio extension to exploit specific characteristics of the SOM based on the topology preservation. As a main result, in contrast to the GHSOM, the inherent hierarchical structure of the data is revealed without requiring the user to define a threshold parameter which controls the map sizes of the individual SOMs. We evaluate our approach using data from real-world data mining projects in the music domain.

DB-HReduction: A data preprocessing algorithm for data mining applications

Data preprocessing is an important and critical step in the data mining process and it has a huge impact on the success of a data mining project. In this paper, we present an algorithm DB-HReduction, which discretizes or eliminates numeric attributes and generalizes or eliminates symbolic attributes very efficiently and effectively. This algorithm greatly decreases the number of attributes and tuples of the data set and improves the accuracy and decreases the running time of the data mining algorithms in the later stage.

Data mining a large health insurance data- base: a follow-up

Reference Gualtierotti [I] describes a data mining problem and a method to tackle it, but says nothing about results as, at the time of publication, none were available. This follow-up contains a description of the first results that were gathered using that method, but only as they inform on data mining particulars rather than on the questions of substance pertaining to the associated data mining project.

Bayesian learning for cardiac SPECT image interpretation

In this paper, we describe a system for automating the diagnosis of myocardial perfusion from single-photon emission computerized tomography (SPECT) images of male and female hearts. Initially we had several thousand of SPECT images, other clinical data and physician–interpreter’s descriptions of the images. The images were divided into segments based on the Yale system. Each segment was described by the physician as showing one of the following conditions: normal perfusion, reversible perfusion defect, partially reversible perfusion defect, fixed perfusion defect, defect showing reverse redistribution, equivocal defect or artifact. The physician’s diagnosis of overall left ventricular (LV) perfusion, based on the above descriptions, categorizes a study as showing one or more of eight possible conditions: normal, ischemia, infarct and ischemia, infarct, reverse redistribution, equivocal, artifact or LV dysfunction. Because of the complexity of the task, we decided to use the knowledge discovery approach, consisting of these steps: problem understanding, data understanding, data preparation, data mining, evaluating the discovered knowledge and its implementation. After going through the data preparation step, in which we constructed normal gender-specific models of the LV and image registration, we ended up with 728 patients for whom we had both SPECT images and corresponding diagnoses. Another major contribution of the paper is the data mining step, in which we used several new Bayesian learning classification methods. The approach we have taken, namely the six-step knowledge discovery process has proven to be very successful in this complex data mining task and as such the process can be extended to other medical data mining projects.

Data Mining; A Conceptual Overview

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.

High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application

We present an application of inductive concept learning and interactive visualization techniques to a large-scale commercial data mining project. This paper focuses on design and configuration of high-level optimization systems (wrappers) for relevance determination and constructive induction, and on integrating these wrappers with elicited knowledge on attribute relevance and synthesis. In particular, we discuss decision support issues for the application (cost prediction for automobile insurance markets in several states) and report experiments using i>D2K, a Java-based visual programming system for data mining and information visualization, and several commercial and research tools. We describe exploratory clustering, descriptive statistics, and supervised decision tree learning in this application, focusing on a parallel genetic algorithm (GA) system, i>Jenesis, which is used to implement relevance determination (attribute subset selection). Deployed on several high-performance network-of-workstation systems (Beowulf clusters), i>Jenesis achieves a linear speedup, due to a high degree of task parallelism. Its test set accuracy is significantly higher than that of decision tree inducers alone and is comparable to that of the best extant search-space based wrappers.

Emerging standards for data mining

This paper presents an overview of data mining, then discusses standards (both existing and proposed) that are relevant to data mining. This includes standards that affect several stages of a data mining project. Summaries of several emerging standards are given, as well as proposals that have the potential to change the way data mining tools are built.

Using A Data Mining Workbench For Micro AndMacro Economic Modelling

Micro economic modelling which includes macro economic factors is often avoided in many commercial data mining projects due to the presence of factors which are difficult to predict and the complexity of bringing together data from diverse sources. In this paper we present a methodology and a set of contrasting algorithms to mine complex data with many unknown factors and for factors whose uncertainty is interrelated. Multiple segmentation, classification techniques and predictive algorithms all play a part in incorporating and controlling uncertainty while guaranteeing precise and reliable information as the end product. To illustrate the approach, we describe a data mining project carried out with the Port of Barcelona, which considers specific business data together with national and international macro-economic indicators.