Data Mining Features

Read what people say are the most valuable features of the solutions they use.
Hilton Rossenrode says in a KNIME review
Business Analyst at a retailer with 501-1,000 employees
* Visual workflow creation * Workflow variables (parameterisation) * Automatic caching of all intermediate data sets in the workflow * Scheduling with the server View full review »
Evans Otalor says in a KNIME review
Business Intelligence Manager at Telecoms
Easy to use nodes for ETL processes. This is because, in many cases, I usually transform the data before the main task even when the data is from a structured database. View full review »
Giovanni Marano says in a KNIME review
Senior Data Scientist
* Easy to connect with every database: We use queries from SQL, Redshift, Oracle. * Easy to have a clear view of the data at every single step of the ETL process, with the consequent possibility of changing the flow according to your needs. View full review »
Wim Michielsen says in a KNIME review
Data Science Consultant
* The very easy-to-use visual interface * Help functions and clear explanations of the functionalities and the used algorithms * Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis For inexperienced analysts or data scientists, it is a very easy tool to take your first steps in modeling and analytics. View full review »
Dusty Evely says in a KNIME review
Business Analyst at a tech services company with 201-500 employees
I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data. View full review »
Agus Kurdiyanto says in a KNIME review
Solution Integrator at a comms service provider with 11-50 employees
Most important, it is open-source. Next is the ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database. View full review »
Prithviraj Dutta says in a KNIME review
Data Scientist at a tech services company with 1,001-5,000 employees
The most useful features are the readily available extensions that speed up the work. For instance, KNIME offers multiple document taggers, which one can use with relative ease. Similarly, the number of predefined NER taggers are also very handy. View full review »
Foundpa67 says in an IBM SPSS Modeler review
Founding Partner at Altdata Analytics
* Automated data cleansing, transformations and imputation of missing data. * Some basic form of feature engineering for classification models, automated binning, etc. This really quickens the model development process. * Automated modelling, classification, or clustering are very useful as well. View full review »
reviewer461148 says in a KNIME review
Partner, Turkey and The Netherlands at a tech vendor with 201-500 employees
* Easy ETL operations * Rich algorithm set * Integrated with other languages like R, Python, and Java. * Works together with other technologies like DeepLearning4j, H2O.ai, D3.js, and Weka. * Ease of use and being a performant solution. * Continues development and wide community support View full review »
Volkan ÇAma? says in an IBM SPSS Statistics review
Principal Consultant at Caligo
Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files. The current features meet with the needs of our company. Our needs are not complex for the features offered. View full review »
reviewer978702 says in a SAS Enterprise Miner review
Founder and CEO with 11-50 employees
Normally I use the SAS 6 Miner, it's a component of SAS Enterprise Miner. It's very useful. View full review »
Ali-Megahed says in an IBM SPSS Statistics review
Senior Statistical Consultant at a financial services firm with 501-1,000 employees
Most of the product features are good but I particularly like the linear regression analysis. I also benefit from the import and export of data abilities. View full review »
Mike Turek says in a SAS Analytics review
Vice President, Business Analysis & Performance at a retailer with 1,001-5,000 employees
The most valuable feature is the ability to handle large data sets. View full review »
Angus Lou says in a KNIME review
Head Of Business Solutions | Unmanned Shop | Automated Retail | AI | IoT | Robotic | Data Science with 51-200 employees
This solution is easy to use and especially good at data preparation and wrapping. It is useful for making a data pipeline to automate data processing tasks. The most valuable feature is to automate what is manually processed. View full review »
Ritchie Poon says in an IBM SPSS Modeler review
Program Director with 11-50 employees
GUI and flow management. View full review »
Ling Li says in a KNIME review
Intern at a energy/utilities company with 10,001+ employees
It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop. View full review »
Miguel Villalobos says in an IBM SPSS Modeler review
Director - Institute of Advanced Analytics at a financial services firm with 501-1,000 employees
It's very easy to use. The drag and drop feature makes it very easy when you are building and testing streams. That's very useful. View full review »
Suebkul Kanchanasuk says in an IBM SPSS Modeler review
Lecturer at a consultancy
New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data. View full review »
SalesEnge92b says in an IBM Watson Explorer review
Sales Engineer at a tech vendor with 501-1,000 employees
Ease of use is pretty good and the standardization of not actually having to have my own natural learning algorithms, just to use the Watson APIs. View full review »
Managerd6bc says in an IBM Watson Explorer review
Manager at a financial services firm with 1,001-5,000 employees
Valuable features are the aggregation mode, that's one of the tool sets. And then, training the models, it can also be utilized for that. View full review »
Technica34e8 says in an IBM Watson Explorer review
Technical Director at a tech vendor with 51-200 employees
The valuable feature of Watson Explorer for us is data entities, and being able to see hidden insights from within unstructured data. View full review »
DevopsEn35ff says in an IBM Watson Explorer review
Devops Engineer at a comms service provider with 1,001-5,000 employees
On the selfish side, possibly advancing my career. But, really, I would say the big thing is being able to utilize some data streams that we haven't tapped into, or maybe not successfully, and being able to produce something that's tremendously useful, not just for one part of the company, but actually company-wide. View full review »
Shannon Orourke says in an IBM Watson Explorer review
Head of Commercialization at Woodside Energy
I'm a user more than I am a developer, but for us it's the ability to ingest and then retrieve information from a range of separate sources; the ability to dissect questions in context and actually answer them. Then separately, the ability for us to then take the output from WEX, I believe - this is me talking to the developers here - taking the outputs from WEX and then processing that to create the visualizations and the analysis that we need. View full review »
Alexandre Martins says in an IBM Watson Explorer review
Architect at a tech services company with 1,001-5,000 employees
Today, integration of voice commands is the focus of development. View full review »
Presiden9a78 says in an IBM Watson Explorer review
President at a tech services company with 11-50 employees
The ability to easily pull together lots of different pieces of information and drill down in a smarter way than has been possible with other analytics tools. Watson is all based on a set of AI and deep learning, machine-learning capabilities, and it is looking behind the scenes at some relationships that you likely would not have spotted on your own. It's pulling things together, categorizing some things, that are not something that you might have seen on your own. View full review »
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