DBMiner [1]:Project Overview [1]Project Description [1]It incorporates several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules and meta-rule guided knowledge mining, etc., and implements a wide spectrum of data mining functions including generalization, characterization, association, classification, and prediction.It performs interactive data mining at multiple concept levels on any user-specified set of data in a database using an SQL-like Data Mining Query Language, DMQL, or a graphical user interface. Users may interactively set and adjust various thresholds, control a data mining process, perform roll-up or drill-down at multiple concept levels, and generate different forms of outputs, including generalized relations, generalized feature tables, multiple forms of generalized rules, visual presentation of rules, charts, curves, etc.Efficient implementation techniques have been explored using different data structures, including generalized relations and multiple-dimensional data cubes, and being integrated with relational database techniques. The data mining process may utilize user- or expert-defined set-grouping or schema-level concept hierarchies which can be specified flexibly, adjusted dynamically based on data distribution, and generated automatically for numerical attributes.Both UNIX and PC (Windows/NT) versions of the system adopt a client/server architecture. The latter communicates with various commercial database systems for data mining using the ODBC technology.Major functional modules [1]:Further Development of DBMiner [1]Methodology [2,3]Results [2,3]CapabilityLearnability/UsabilityInteroperabilityFlexibilityTwo criteria can be defined to explain the flexibility of the application namely if the work environment is customizable and whether it is possible to write or change the code.DBMiner uses DBQL for its internal functionality, however it is not possible to change or write DBQL.DBMiner has the flexibility to let the user change the values of settings after each task is done. For example, it is possible to increase/decrease the support threshold or the confidence threshold if the user is not happy with the current level.Other LimitationsDBMiner depends only on MS SQL Server as its back-end and uses MS Excel 2000 as its visualization tool for OLAP browsing. Other unavailable functional modules are data dispersion module, time-serial analysis module, and prediction module.ConclusionsDBMiner is a good data-mining tool as it reflects a user-friendly environment for users of all category. The discussion above about the software substantiates our evaluation about the software though there is a wide scope of improvement for the commercial version.References[1] Copied and pasted from “DBMiner: A data mining tool for large relational databases,” http://db.cs.sfu.ca/sections/projects/dbminer.html[2] Bhavani Thuraisingham, Data Mining: technologies, techniques, tools, and trends, CRC press, 1999[3] John F. Elder IV & Dean W. Abbott Elder Research, A Comparison of Leading Data Mining Tools, 1998Appendix ATable 1: Capability, Learnability/Usability, Interoperability, and FlexibilityExcellentGoodAverageNeeds ImprovementPoorDoes Not ExistScalabilityHas programming languageProvides useful output reportsVisualizationWizardsEasy to learnUser’s manualOnline helpInterfaceImporting dataExporting dataLinks to other applicationsCustomizable work environmentAbility to write or change codesOverallDBMiner [1]:A data mining tool for large relational databases [1]DBMiner, a data mining system for interactive mining of multiple-level knowledge in large relational databases, has been developed based on our years-of-research. The system implements a wide spectrum of data mining functions, including generalization, characterization, discrimination, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance. Project Overview [1]A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. It is based on studies of data mining techniques and experience in the development of anearly system prototype, DBLearn. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of severalinteresting data mining techniques, including attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance. Project Description [1] Figure: General architecture of DBMinerThe system has the following distinct features: - It incorporates several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules and meta-rule guided knowledge mining, etc., and implements a wide spectrum of data mining functions includinggeneralization, characterization, association, classification, and prediction.- It performs interactive data mining at multiple concept levels on any user-specified set of data in a database using an SQL-like Data Mining Query Language, DMQL, or a graphical user interface. Users may interactively set and adjust various thresholds, control a data mining process, perform roll-up or drill-down at multiple concept levels, and generate different forms of outputs, including generalized relations, generalized feature tables, multiple forms of generalized rules, visual presentation of rules, charts, curves,etc.- Efficient implementation techniques have been exploredusing different data structures, including generalized relations and multiple-dimensional data cubes, and being integrated with relational database techniques. The data mining process may utilize user- or expert-defined set-grouping or schema-level concept hierarchies which can be specified flexibly, adjusted dynamically based on data distribution, and generated automatically for numerical attributes.- Both UNIX and PC (Windows/NT)