Use SQL / database system (116) 37.8%

Do data manipulation within data mining tool (95) 30.9%

Excel (84) 27.4%

SAS (75) 24.4%

Java (50) 16.3%

R (39) 12.7%

Perl (39) 12.7%

MATLAB (35) 11.4%

Python (34) 11.1%

C++/C# (30) 9.8%

shell/awk/gawk (29) 9.4%

C (19) 6.2%

Other (17) 5.5%

Other statistical languages (15) 4.9%

S-PLUS (11) 3.6%

Other compiled languages (11) 3.6%

Other scripting languages (9) 2.9%

Ruby (1) 0.3%

 ─脚本语言（Perl、Python、 Ruby、awk）

 在这些响应者中，用数据库、Excel和SAS系统管理数据的人最多，但看着这些人不乏程序员，不但有用传统的编译语言C、Java，还有用脚本语言Perl、Python、Ruby，甚至还有用流语言awk的。

Commercial Data Mining Software

SPSS Clementine 116, 73 alone or with SPSS

Salford CART/MARS/TreeNet/RF   106, 54 alone

Excel  94, 2 alone

SPSS  91, 49 alone or with Clementine

SAS  80, 8 alone or with SAS E-Miner

Angoss  78, 50 alone

KXEN   70, 51 alone

SQL Server  38, 2 alone

MATLAB  30, 1 alone

SAS E-Miner  25, 8 alone or with SAS

Other commercial tools  21, 0 alone

Statsoft Statistica  15, 2 alone

Insightful Miner/S-Plus  14, 0 alone

Oracle DM  12, 0 alone

Tiberius  11, 3 alone

FairIsaac Model Builder  3, 2 alone

Xelopes  2, 2 alone

Miner3D  2, 0 alone

Bayesia  2, 0 alone

Megaputer  1, 1 alone

your own code 61, 7 alone

商业数据挖掘软件包

1.这些商业数据挖掘工具大概包括：

 —统计软件包和来自统计软件包生产厂商的数据挖掘套件SPSS、SAS、Statistica、S-Plus、SPSS Clementine、SAS E-Miner、Insightful Miner

 —其他专业的数据挖掘软件包，SalfordCART/MARS/TreeNet/RF、

Angoss、KXEN  

 —电子表格Excel

 —数学软件Matlab

 —特定行业的数据挖掘软件包FairIsaac Model Builder （金融业）

 —基于数据库的数据挖掘套件

SQL Server（应该是其中的Analysis Service）、Oracle DM

2.看着几乎是统计软件包及其相应的数据挖掘套件如SPSS和SAS的天下，基于数据库的如SQL Server、Oracle DM 也有一席之地。

Free Data Mining Software

Yale  103, 70 alone

Weka  48, 3 alone

R  42, 0 alone

Other free tools  30, 0 alone

C4.5/C5.0/See5  14, 0 alone

Orange  12, 0 alone

KNIME  2, 0 alone

Decision Trees/Rules (127) 62.6%

Regression (104) 51.2%

Clustering (102) 50.2%

Statistics (descriptive) (94) 46.3%

Visualization (66) 32.5%

Association rules (53) 26.1%

Sequence/Time series analysis (35) 17.2%

Neural Nets (35) 17.2%SVM (32) 15.8%

Bayesian (32) 15.8%

Boosting (30) 14.8%

Nearest Neighbor (26) 12.8%

Hybrid methods (24) 11.8%

Other (23) 11.3%

Genetic algorithms (23) 11.3%

Bagging (22) 10.8%

Regression（回归）、Statistics (descriptive)（描述性统计）、

Boosting（）、Visualization（可视化）

  ─分类

Decision Trees/Rules（决策树和规则）、Neural Nets（神经网络）、Bayesian（贝叶斯）、Genetic algorithms （遗传算法）

─聚类

Clustering（聚类）、Nearest Neighbor（最近邻）

─关联（Association rules）

─时间序列（Sequence/Time series analysis ）
─其他(Hybrid methods、Bagging)

2.结论之一，简单的、直观的、容易解释的方法用得较多，比如决策树、回归、描述性统计……

CRM (36) 26.1%

Banking (33) 23.9%

Direct Marketing/ Fundraising (28) 20.3%

Science (26) 18.8%

Fraud Detection (26) 18.8%

Telecom (21) 15.2%

Credit Scoring (19) 13.8%

Other (18) 13.0%

Biotech/Genomics (16) 11.6%

Web usage mining (14) 10.1%

Retail (14) 10.1%

Medical/ Pharma (13) 9.4%

Insurance (12) 8.7%

Health care/ HR (10) 7.2%

Government/Military (10) 7.2%

Financials/Lending (10) 7.2%

Web content mining/Search (9) 6.5%

Manufacturing (9) 6.5%

e-commerce (8) 5.8%

Entertainment/ Music (6) 4.3%

Social Policy/Survey analysis (5) 3.6%

Security / Anti-terrorism (5) 3.6%

Investment / Stocks (4) 2.9%

Travel/Hospitality (3) 2.2%

Junk email / Anti-spam (3) 2.2%