Classification Of Relational Data Mining Tools
Universal tools, which include methods of classification and preliminary data preparation, concern to the category relational data mining. This group consists of well-known commercial tools, such as:
1. DBMiner 2.0 Enterprise, which is a powerful tool for research of greater databases. It uses Microsoft Server SQL 7.0 Plato.
2. Another sample of commercial tools is IBM Intelligent Miner for Data. This tool offers the last Data Mining methods, supports full Data Mining process: from preparation of data up to presentation of results. It also offers support of languages XML and PMML.
3. KXEN (Knowledge eXtraction ENgines) is a tool working on the basis of SVM theory. It solves problems of data preparation, segmentation, temporary numbers and SVM-classification.
4. Oracle Data Mining provides GUI, PL/SQL-interfaces and Java-interface. Used methods of this tool are algorithms of associative rules search, SVM and others methods.
5. Polyanalyst is a set providing all-round Data Mining. Now, besides the methods of former versions, it also includes the analysis of texts, decisions, and relations. It supports OLE DB for Data Mining and DCOM-technology.
6. SAS Enterprise Miner is an integrated set, which provides friendly GUI. The SEMMA methodology is supported.
7. Statistica Data Miner is a tool, which provides the all-round, integrated statistical data analysis. It has powerful graphic opportunities, management of databases, and also the appendix of system engineering.
The information in data warehouse needs not only to be centralized and structured only. Means of visualization of this information, the tool by means of which it is easy to obtain the data necessary for acceptance of duly decisions are necessary to the analyst. One of the main requirements of any analyst is simplicity of report formation and its presentation.
In case of operative systems construction of reports is often deprived flexibility; to create a new report, it is necessary to involve IT experts, who unite the data of several systems. In case of use of data warehouse the decision of a problem is given by OLAP (On-Line Analytical Processing) technology. This technology provides access to data in terms, habitual for the analyst. OLAP technology is based on the concept of multivariate data presentation.
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