Soft computing for intelligent data analysis

Intelligent data analysis (IDA) is an interdisciplinary study concerned with the effective analysis of data. The paper briefly looks at some of the key issues in intelligent data analysis, discusses the opportunities for soft computing in this context, and presents several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consistency checking between mass spectral data with proposed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deterioration, and diagnosis in an oil refinery involving multivariate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies

An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

Intelligent data analysis of clinical trials with Stata

Clinical trial statistical analysis and reporting is a formidable task. A final-study report requires the creation of hundreds of tables and data listings, and the calculation of over one thousand statistical significance levels, difference estimates, and confidence limits. Typically, several database programmers, statistical programmers, and biostatisticians are needed to perform this task over a period of time that is measured in months. I describe the design approaches and the evaluation of an intelligent data analysis system (DART) that automates the creation of clinical trial statistical reports, which is one component of an integrative Clinical Trials Information System. This application was developed in Stata programming language and has about 9,000 lines of code. This unsupervised knowledge-based system is able to select, according to the characteristics of the study design, the study statistical analysis plan and the type of baseline and efficacy variables used (which are all encoded and stored in the database), the statistical methods adequate for each analysis, and the results that need to be reported. The entire process of data analysis and reporting can be performed automatically, or the user may specify some parameters of the analysis (e.g., scale transformations, adjustment for confounding). The application can handle commonly used statistical methods applied to clinical trials analyses for nominal, multi-valued, ordinal, interval, and event/count data in one-, two-, and multiple-arm trials, crossover studies, and factorial designs, with or without stratification. It handles imputation of missing data, scale transformations, and regrouping of study centers. It can automatically select baseline variables for inclusion as covariates, and conduct poststratification analyses and subgroup analyses. So far, DART has been successfully used for the automated statistical reporting of 35 pharmaceutical clinical trials. In a validation study, the statistical methods used in a random sample of 51 clinical trials were published in The New England Journal of Medicine and in The Lancet, reporting 97 different analyses. The analytical methods were identical or equivalent to those selected by DART in 84.5% of the analyses, different from DART in 6.2%, and not supported by DART in 9.3%.

Automated post-fault diagnosis of power system disturbances

In order to automate the analysis of SCADA and digital fault recorder (DFR) data for a transmission network operator in the UK, the authors have developed an industrial strength multi-agent system entitled protection engineering diagnostic agents (PEDA). The PEDA system integrates a number of legacy intelligent systems for analyzing power system data as autonomous intelligent agents. The integration achieved through multi-agent systems technology enhances the diagnostic support offered to engineers by focusing the analysis on the most pertinent DFR data based on the results of the analysis of SCADA. Since November 2004 the PEDA system has been operating online at a UK utility. In this paper the authors focus on the underlying intelligent system techniques, i.e. rule-based expert systems, model-based reasoning and state-of-the-art multi-agent system technology, that PEDA employs and the lessons learnt through its deployment and online use

Intelligent data analysis - support for development of SMEs sector

The paper studies possibilities of intelligent data analysis application for discovering knowledge hidden in small and medium-sized enterprises’ (SMEs) data, on the territory of the province of Vojvodina. The knowledge revealed by intelligent analysis, and not accessible by any other means, could be the valuable starting point for working out of proactive and preventive actions for the development of the SMEs sector.Intelligent data analysis, CRISP-DM, clustering, small and medium enterprises., Research and Development/Tech Change/Emerging Technologies, C8, L2,

Incorporating the knowledge management cycle in e-business

In e-business, knowledge can be extracted from the recorded information by intelligent data analysis and then utilised in the business transaction. E-knowledge is a foundation for e-business. E-business can be supported by an intelligent information system that provides intelligent business process support and advanced support of the e-knowledge management cycle. Knowledge is stored as knowledge models that can be updated in the e-knowledge management cycle. As illustrated in examples, the e-knowledge cycle aids in the business decision taking, production management, and costs management

Embedded intelligence for electrical network operation and control

Integrating multiple types of intelligent, mulitagent data analysis within a smart grid can pave the way for flexible, extensible, and robust solutions to power network management

Intelligent data analysis approaches to churn as a business problem: a survey

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft