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Data Mining / Analytic Software Tools

Data Mining (Analytic) tools you used in 2007: [534 voters]

The first (narrow) bar corresponds to the number of votes where the tool was selected alone, and the second (wide) bar to the number votes where the tool was select as one among several; Tools are ordered in descending order of total number of votes
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
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

For comparison, here are the results of 2006 KDnuggets Poll on Data mining/analytic tools


data miner, Price versus value
I do not believe that bigger companies buy SAS due to quality, scalability and usability, but rather on inertia and the belief that if they pay the most money, they get the best product.
The value of a data mining workbench should be improved productivity through the use of innovative algorithsm that minimize the modeling effort.
Data Mining tools were never intended to be used only by statisticians and computer scientists. Unfortunately, most tools available in the market today require advanced skills in order to use effectively.
No - I don't want to dumb the user down... But simply adding more, equally inefficient algorithms into a data mining tool does not make it a better tool!
Value is based on improved productivity, not another novel algorithm.

(Editor: for fairness, free tools like Weka offer many more algorithms than commercial tools like SAS or SPSS. In my experience, companies buy commercial tools for many reasons, including better interfaces, reliability, ease of use, scalability - rarely just for more algorithms.)

I start to use KXEN when i study datamining in zhejiang university. I think KXEN is very good at Algorithm compare to other tools such as WEKA,DB miner etc.

Terry Taerum, Cost / Value of information
I think the previous comment about the "Swiss army knife" is a good one. I suspect though the purchase of a data mining tool is driven as much by having the "right tool" as the cost of the tool. There is also the issue of "my language" and "my interface" and "my application" - which possibly explains why so many data mining applications come out of existing database and statistical programs (e.g. SQL, ORACLE, SPSS, SAS to name but a few...). Finally, there is the question of value of information and cost of information. Information tends to zero. That is, as it gets olders, as it gets known by others, as it disintegrates, it becomes less valuable. The cost also tends to zero. And this places a limit on what should be spent on an application. So in pulling a tool out of my favorite tool box, the right tool should be the most important issue from my point of view.

Ross Bettinger, Data Mining Tools
It would be interesting to cluster data mining tool chosen using size of company (revenue or equivalent measure) because the cost of the tool may be a factor in its adoption. SAS E-Miner is a Swiss army knife of capabilities but it costs a bundle, while a more moderately-priced tool that does not offer as many algorithms or features may be used because it is more affordable.

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