IBM Smart Analytics vs Weka

IBM Smart Analytics is ranked 8th in Data Mining with 1 review vs Weka which is ranked 5th in Data Mining with 0 reviews. The top reviewer of IBM Smart Analytics writes "Adding LA on top of a well deployed & working Tivoli Framework opens up a flood of native logged data points. The visual presentation layer of LA is less than cutting edge". IBM Smart Analytics is most compared with Oracle Advanced Analytics, SAP KXEN and IBM SPSS Statistics. Weka is most compared with KNIME, IBM SPSS Modeler and SAS Analytics.
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Most Helpful Review
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Quotes From Members Comparing IBM Smart Analytics vs Weka

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
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Find out what your peers are saying about IBM, Knime, SAS and others in Data Mining.
254,138 professionals have used our research since 2012.
Ranking
RANKING
Views
823
Comparisons
202
Reviews
1
Followers
268
Avg. Rating
7.0
Views
4,908
Comparisons
3,798
Reviews
0
Followers
370
Avg. Rating
N/A
Top Comparisons
Top Comparisons
Sap065658 logo 300x300 0r 01
Compared 9% of the time.
See more IBM Smart Analytics competitors »
Uimhresp 400x400
Compared 28% of the time.
Ibm
Compared 16% of the time.
Sas
Compared 10% of the time.
See more Weka competitors »
Also Known As
Also Known AsSmart Analytics
Website/Video
Website/VideoIBM
  • Vendor 7400 screenshot 1514653938
Weka
  • Vendor 29286 screenshot 1519514357
OverviewQuestionmark icon
Overview
Information Not Available
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Sample Customers
Sample CustomersWIdO AOK, EEKA Fashion, SSGC, GS Retail
Information Not Available
Data mining report from it central station 2018 01 27 thumbnail
Find out what your peers are saying about IBM, Knime, SAS and others in Data Mining.
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254,138 professionals have used our research since 2012.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.

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