The Top 10 Data Mining Tools Of 2019

For most businesses and companies keeping their data in the correct order is absolutely necessary. Moreover, data is an important tool for them as they can also derive important information from the same.

Also known as data mining this process is very crucial for any company to succeed. There are multiple data mining tools which firms can use to accomplish this simple yet arduous task. To assist you in making the correct choice we analysed the best data mining tools available for use.

We made a note of their features and pros and cons and made it easy for you to choose from among the lot.

Hopefully, you would be able to read through our analysis and figure out which data mining tool is perfect for your task. Once that is done you can use the tool to explore multiple opportunities with respect to your data and use it to your advantage.

 

1. Rapid Miner

RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

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Review: FinancesOnline

Rapidminer offers a robust and very powerful integrated suite of tools and features, with each component parading a user-friendly interface that helps users realize massive productivity gains right from the get-go. Its visual workflow designer tool offers users with an easy to use visual environment that enables them to design, create, and deploy analytics processes, visual presentations, and models without breaking a sweat.

With rapidminer, uncluttered, disorganized, and seemingly useless data becomes very valuable. The system simplifies data access and manager, allowing you to access, load, and evaluate all sorts of data, including texts, images, and audio tracks. Rapidminer lets you structure them in a way that it is easy for you and your team to comprehend.

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2. Orange

Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, mds, and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections.

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Review: Capterra

Pros: the graphical user interface of orange is great for someone who is not a programmer but wants to execute analytic workflows on machine learning on their dataset. The interface is nicely designed and the analytics workflow is easy to create with the use of drag and drop of its widgets. The widgets available are extensive and would enable users to clean, visualize, build models for supervised and unsupervised learning and validate their model. Orange has put together resources for its users to pick up machine learning on their own. There are also several tutorial videos as well on the website.

Cons: we aren’t able to set.Seed in the software which causes results and analysis to not be reproducible. Orange does not provide enough parameters to tweak for advanced users. For instance, the random forest widget does not allow the user to see which variables have the highest information gain.

Overall: overall my experience with orange has been fruitful for me as I picked up machine learning concepts without allowing my basic coding knowledge to hinder me. The interface is prettier than weka(another GUI tool for machine learning) there are several preloaded datasets that have already been clean and users can take advantage of those and follow through the documentation that is provided. My favorite one is the Titanic dataset that predicts if the person survives or not depending on their gender, class and the number of siblings/spouses they have.

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3. Weka

Waikato Environment for Knowledge Analysis is a suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License.

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Review: Capterra

Pros: Weka is powerful and easy to use data mining software. Currently, it is open source. The free online courses associated with Weka are very valuable.

Cons: The file format for Weka is not very popular. There is a learning curve. An intelligent assistant system would be very useful to help people understand errors.

Overall: Weka is free and powerful. I used it to illustrate data mining concepts.

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4. KNIME

Knime, the Konstanz information miner, is a free and open-source data analytics, reporting and integration platform. Knime integrates various components for machine learning and data mining through its modular data pipelining concept

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Review: FinancesOnline

KNIME Analytics Platform is a scalable data analytics solution that allows you to toggle smoothly between one computer, streaming, and big data execution. The software provides workflow controls, mathematics & statistical functions, machine learning algorithms, advanced predictive algorithms, and more to streamline data scientists’ tasks. Its intuitive graphical user interface is built to amplify the ease of use and help you create data flows, implement specific analysis techniques, and analyze models, results, and interactive views with ease.

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5. SSDT

SSDT is a universal, declarative model that expands all the phases of database development in the Visual Studio IDE. BIDS was the former environment developed by Microsoft to do data analysis and provide business intelligence solutions. Developers use SSDT transact- a design capability of SQL, to build, maintain, debug and refactor databases.

A user can work directly with a database or can work directly with a connected database, thus, providing on or off-premise facility.

Users can use visual studio tools for the development of databases like IntelliSense, code navigation tools, and programming support via, visual basic, etc. SSDT provides Table Designer to create new tables as well as edit tables in direct databases as well as connected databases.

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Review: Gartner

I have more than 5 years of professional experience as a software developer with Microsoft SQL Server Integration Services (SSIS). This product helped me with data migration from legacy data sources to brand new ones when I was working on several enterprise level financial projects. It works perfectly well with traditional relational data sources along with non-relational data. Debugging features, intuitive graphic user interface and supporting C# along with VB.NET are a big plus as well.

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6. Apache Mahout

Apache Mahout

Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends.

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Review: G2

What do you like best?
It is open source, developed under an ideology or paradigm similar to that of big data, supports or was created for scalable algorithms such as matrix factorization, collaborative filtering, used in global companies such as yahoo, clouster storage, integration with other tools.

What do you dislike?
Exposed to failures, it does not cover all the algorithms, it is not used in all the companies it is counted the companies that use it

Recommendations to others considering the product
Use it, easy use, easy admin, quality of support

What business problems are you solving with the product? What benefits have you realized?
Algorithms such as filtering, matrices of salaries and distribution of the social economic part of the company.

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7. Rattle

The rattle is a popular GUI for data mining using r. It presents statistical and visual summaries of data, transforms data so that it can be readily modeled, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically, and scores new datasets for deployment into production. A key feature is that all of your interactions through the graphical user interface are captured as an r script that can be readily executed in r independently of the rattle interface. Use it as a tool to learn and develop your skills in r and then to build your initial models in rattle to then be tuned in r which provides considerably more powerful options.

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Review: Metacritic

Rattle’s greatest accomplishment isn’t to just write music for drums but to write songs that capture AND transcend this modern era we live in. The duo’s sense of freedom and unwillingness to mimic the tropes of conventional songwriting are to be admired, even if they’re not necessarily traits that will convince anyone but ardent early-Reich fans that drumming records are worthy of a place on their shelf.

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8. DataMelt

Datamelt, also known as Dmelt is a computation and visualization environment that provides an interactive framework to do data analysis and visualization. It is designed mainly for engineers, scientists & students.

Dmelt is written in Java and it is a multi-platform utility. It can run on any operating system which is compatible with JVM(java virtual machine).

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Review: SOURCEFORGE

I’ve just downloaded version 2.2 and it’s really cool. I can run my python programs and modify them by adding classes from the java. I especially like the new feature in this release called search dmelt. This feature allows me to search in the huge repository of code examples of the dmelt project. I also like the data visualization capabilities of the java packages which are far more than I expected. I really recommend this product for data analysis and data mining.

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9. IBM SPSS Modeler

The IBM spss software platform offers advanced statistical analysis, a vast library of machine-learning algorithms, text analysis, open-source extensibility, integration with big data and seamless deployment into applications. Its ease of use, flexibility, and scalability make IBM spss accessible to users with all skill levels and outfits projects of all sizes and complexity to help you and your organization find new opportunities, improve efficiency and minimize risk.

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Review: G2

What do you like best?
This is a very easy, intuitive software to use to connect multiple databases and sources together and transform data as well as apply different statistical forecasting methods to it. It is point and clicks so you don’t need to write code.

What do you dislike?
I cannot think of anything that I didn’t like about the product.

Recommendations to others considering the product
It’s a bit pricey to get licenses for an entire team. Work with IBM to negotiate concurrent user licenses on remote computers.

What business problems are you solving with the product? What benefits have you realized?
This software helps us bring our Abacus RFM scoring model in house and convert our segmenting to a predictive model on each customers buying behavior. This saved us over $300,000 annually by not having to pay outside vendors for this type of monthly analysis.

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10. Teradata

Teradata Corporation is a provider of database and analytics-related products and services. The company was formed in 1979 in Brentwood, California, as a collaboration between researchers at Caltech and Citibank’s advanced technology group.

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Review: TrustRadius

Teradata is used by most of the departments in business enterprise. It addresses the processing of large amounts of data with ease.

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