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特點： |
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- 支援完整Data mining(資料採礦)，功能包括分類、推估、預測、關聯、群集
- 支援微軟資料倉儲聯盟通訊介面OLE DB
- 能透過SQL 查詢語法評分資料倉儲內資料
- 具Multithreaded
演算法做為資料平行處理
- 具有Client/
Server架構
- 具有SDK工具，提供COM物件單獨機器演算法，易於整合商業應用
- 完整前後處理功能包含載入、分析、報告、評分、產生、DHTML
/ XML報告
- 唯一具有下一代Data mining(資料採礦)SKAT (Symbolic
Knowledge Acquisition Technology)
解決多維度相依問題
- 具有19種機器學習演算法:
1.
Summary Statistics - (Major exploratory statistics)
2. PA Scheduler - (Batch-process data mining
manager)
3. Linear Regression - (Stepwise Linear Regression)
4. Find Dependencies Algorithm - (N-dimensional
distribution analysis)
5. Discriminate algorithm - (Provided only together
with CL)
6. Visual Charts - (Histograms, 2-D Charts,
3-D Charts, and Snake Charts)
7. Link Chart - (Reveals positive and negative
pairwise correlations)
8. Text Analysis - (Linguistic, Semantic and
Machine Learning Analysis)
9. Text Categorization - (Linguistic, Semantic
and Machine Learning Analysis)
10. Link Terms - (Linguistic, Semantic and Machine
Learning Analysis)
11. Link Analysis - (Reveals positive and negative
pairwise correlations)
12. Decision Forest - (Efficient classification
to numerous classes)
13. Find Laws Algorithm - (Symbolic Knowledge
Acquisition Technology)
14. PolyNet Predictor Algorithm - (GMDH-Neural
Net hybrid)
15. PAY Algorithm - (Memory Based Reasoning
and Genetic Algorithms hybrid)
16. Decision Tree algorithm - (Information Gain
- IG7)
17. Cluster Algorithm - (Localization of Anomalies)
18. Classify Algorithm - (Fuzzy logic )
19. Market Basket Analysis - (Transactional
clustering and association rules)
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SKAT 作業流程 |
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