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 TitleDate AddedCompany
whitepaper Performing a Data Mining Tool Evaluation2009-06-01 SPSS
  Data mining helps you make better decisions that result in better outcomes for your organization. This paper provides a checklist to help you evaluate data mining tools according to CRISP-DM, the cross-industry standard process model for carrying out data mining projects, and choose the best one for your needs.

Tags: Data Tools, Data Tools, Data Infrastructure, Data Infrastructure		   
whitepaper Getting smart about ROI: measuring the impact of business intelligence2009-06-01 SPSS
  Organizations looking to return to business basics might consider business intelligence (BI) solutions for better decision making. BI solutions are designed to help companies efficiently access and analyze data in order to operate more efficiently. BI solutions can be valuable in analyzing the effectiveness of ongoing operations and special initiatives alike. And so BI can make an important contribution to better management. This white paper provides some approaches that will help companies evaluate BI initiatives. By doing so, they can see what value these solutions may hold for their organization and make more informed decisions about whether to implement them, and how.

Tags: Data Tools, Enterprise Planning, Data Tools, Data Tools		   
whitepaper Predictive Analytics for the Detection of Suspicious Claims2009-06-01 SPSS
  Predictive analytics helps streamline the process of identifying potentially fraudulent claims, increasing the efficiency of investigators while rewarding honest customers with faster claims payouts. To learn more, download this white paper.

Tags: Enterprise Planning, Data Tools, Data Infrastructure, Data Tools		   
whitepaper 2D CAD Data Mining Based on Spatial Relation2009-05-30 Institute of Electrical and Electronics Engineers
  This paper proposes CAD data mining technique to obtain semantic elements without prior knowledge about plans being designed. The method consists of two steps. The first step is to extract frequent spatial relations between figure elements in CAD data as clues to the semantic elements. These relations are modeled as topology graph and are analyzed by a graph mining method. In the second step, valid semantic elements are specified by eliminating geometrically unnecessary figure elements through inferring every affine transformation between sets of figure elements having the same frequent spatial structure. In the experiments, the proposed method could extract semantic elements like electrical symbols from floor plan data without prior knowledge about the symbols.

Tags: Software Development Tools		   
whitepaper RE-EM Trees: A New Data Mining Approach for Longitudinal Data2009-05-29 New York University
  Longitudinal data refer to the situation where repeated observations are available for each sampled individual. Methodologies that take this structure into account allow for systematic differences between individuals that are not related to covariates. A standard methodology in the statistics literature for this type of data is the random effects model, where these differences between individuals are represented by so-called "Effects" that are estimated from the data. This paper presents a methodology that combines the flexibility of tree-based estimation methods with the structure of random effects models for longitudinal data.

Tags: Data Tools		   
whitepaper Debellor: Open Source Modular Platform for Scalable Data Mining2009-05-25 Warsaw University
  This paper introduces Debellor (www.debellor.org) - an open source extensible data mining platform with stream-oriented architecture, where all data transfers between elementary algorithms take the form of a stream of samples. Data streaming enables implementation of scalable algorithms, which can efficiently process large volumes of data, exceeding available memory. This is very important for data mining research and applications, since the most challenging data mining tasks involve voluminous data, either produced by a data source or generated at some intermediate stage of a complex data processing network. Advantages of data streaming are illustrated by experiments with clustering time series.

Tags: Desktop Client OS		   
whitepaper Research on Intelligent Decision Oriented Architecture of Data Mining2009-05-24 Academy Publisher
  The large commercial transactions data implied lots of useful knowledge for business decisions, the hidden patterns and relationships could be found by data mining. This paper analyzed and pointed out the characteristics and the deficiency of the general architecture of the data mining, the general architecture of data mining was improved by the parallel processing technology and the results of data mining be saved. The parallel architecture of the data mining which made for business intelligence applications with the model storage was presented, and its characteristics were analyzed, the analysis showed that the parallel architecture of data mining has a possibility for business intelligence.

Tags: Data Tools		   
whitepaper Finding Diamonds in Data: Reflections on Teaching Data Mining From the Coal Face2009-05-21 University of Wisconsin-Milwaukee
  Making sense of the exponentially expanding sources of structured electronic data collected by organizations is increasingly difficult. Data mining is the extraction of implicit, previously unknown, and potentially useful information from large volumes of such data to support decision-making in organizations and has led to an increase in demand for students who have an understanding of data mining techniques and can apply them to organizations' data. Thus data mining is an increasingly important component of the Information Systems curriculum in order to meet this skills demand.

Tags: Data Tools		   
whitepaper Data Mining in the Real World: Experiences, Challenges, and Recommendations2009-05-13 Fordham University
  Data mining is used regularly in a variety of industries and is continuing to gain in both popularity and acceptance. However, applying data mining methods to complex real-world tasks is far from straightforward and many pitfalls face data mining practitioners. However, most research in the field tends to focus on the algorithmic issues that arise in data mining and ignores the human element and process issues that are often the cause of these pitfalls. While there are some papers on data mining experiences and lessons learned, they are quite rare, especially in the research community. The purpose of this paper is to begin to fill in the "Gap" between data mining methods and the practice of data mining.

Tags: Data Tools		   
whitepaper APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining2009-05-03 University of Central Florida
  While the emerging field of Privacy Preserving Data Mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering PPDM applications. The individual parties involved in the data mining process need a way to bring high-performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses APHID (Architecture for Private and High-performance Integrated Data mining), a practical software architecture for developing and executing large-scale PPDM applications. At one level, the system supports simplified use of cluster and grid resources, and at another level, the system abstracts communication for easy PPDM algorithm development.

Tags: Data Infrastructure