XpertRule® Miner 資料採礦軟體-資安軟體/研究分析軟體/心理學軟體/新永資訊有限公司

XpertRule® Miner 資料採礦軟體

XpertRule® Miner 資料採礦軟體

  • XpertRule® Miner 資料採礦軟體
  • 編號
  • 類別
    統計分析軟體
  • 介紹
    XpertRule Miner所採用之資料採礦(data mining)是最近學術界與產業界十分重視的技術,它是藉由資訊技術從大量資料裡粹取出資訊與結果之間的關聯性。企業可以用它得到顧客與購買產品之間的相關資訊。現今大多數企業在分析他們的客戶資料時,採取傳統統計方法所做出的結果,仍具有改進的空間。
  • 價格

XpertRule® Miner 資料採礦軟體

XpertRule Miner所採用之資料採礦(data mining)是最近學術界與產業界十分重視的技術,它是藉由資訊技術從大量資料裡粹取出資訊與結果之間的關聯性。企業可以用它得到顧客與購買產品之間的相關資訊。現今大多數企業在分析他們的客戶資料時,採取傳統統計方法所做出的結果,仍具有改進的空間。

XpertRule® Miner is XpertRule Software's next generation product evolved from the established Profiler scalable client-server data mining software. Using ActiveX technology, the Miner client can be deployed in a variety of ways. Solutions can now be built as stand-alone mining systems or embedded in other vertical applications under MS-Windows. Deployment can also be over Intranets or the Internet. The ActiveX Miner client works with XpertRule's high performance data mining servers to provide multi-tier client-server data mining against very large data bases. Mining can be performed either directly against the data in situ, or by high performance mining against tokenised cache data tables.
Miner includes extensive data transformation, visualisation and reporting features. Data can be manipulated using a drag and drop interface. Users can graphically design their customized data manipulation, mining and reporting processes. Software developers can also directly control the application using the exposed methods and properties of the Miner's objects. This enables Miner to be seamlessly integrated as part of vertical applications - such as for a Customer Relationship Management system, which could have been built in any environment (VB, Delphi, etc.). All this is achieved without compromising scalability or performance.
With this flexible deployment, applications can now be delivered as stand alone, embedded within another software environment or as Intranet/Internet enabled applications.
This example shows the ProfilerX tree client running over the Intranet using the Microsoft IE 4 browser. Miner will generate a default HTML/script page with an embedded ActiveX component.
Reporting facilities within Miner are extensive. Visualisation and exploration of your data can be incorporated at any stage of the data mining life cycle.
Data can be filtered through tree profiles and reported on, or extracted to new data sources. Graphs, charts and tables can be dynamically linked to the graphical tree views of your data. As the user navigates through different levels and applies different scenarios they can be kept constantly updated.
XpertRule Miner now provides both the feature rich environment required by the experienced data mining practitioner, as well as enabling highly focussed mining and reporting systems to be provided for users who are new to data mining. 

interface, data transformations can be designed and executed. Interim reports can be included to monitor progress, using expand/collapse data structure tables and 3D data exploration graphs. Data can be input from any ODBC compliant data source and output as ODBC, CSV or DMT format files.

Table Manipulations
  • • Table joins: The data fields from different tables can be joined together according to a common index field.
  • • Table merge: The rows from different tables with the same field definitions can be merged.
  • • Table filtering: Rows can be excluded from tables according to a given criteria. They can also be filtered through trees to find out which profile they belong to.
  • • Table sorting: Tables can be sorted into ascending or descending order.
  • • Table sampling: Random samples can be extracted from larger tables.
Field manipulations
  • • Derived fields: New fields can be generated from calculations or string manipulations on existing fields. This can be used to clean data fields or to generate new fields for data mining. Features drag and drop of field variables and Visual Basic like syntax.
  • • Aggregated fields: New fields can be generated by aggregating the data in other data fields to generate summaries such as minimum, maximum, average, sum and count. Time series aggregation can also be performed.

 
 

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