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Data always remains as raw until and unless it is being mined, and the information contained within it is harnessed. Mining data to make sense of it has applications in a wide range of academic and industry. In this article, we are now going to explore the best open source tools that can even aid us in the process of data mining.
There are lots of data mining tools that are available to use.
Data mining, which is also known by the name as knowledge discovery from the databases, is one of the essential processes of extraction and analyzing enormous amounts of data and extracting information from it. Data mining can quickly answer the business questions that would even have otherwise consumed a lot of time.
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Some of its applications include the market segmentation like the identifying characteristics of a customer buying of a particular product from a specific brand, fraud detection – identifying transaction patterns that could even probably result in online fraud, trend analysis 00 what products or services are always purchases together and market-based trend, etc.
This article focuses on the full range of open sources and paid options that are available and their significance in the different contexts.
For all those people, who are just new to data mining, let us take a brief look at some of the tasks which are common to data mining.
Pre-processing: This task provided with all the preliminary tasks that can help in getting started with any of the actual mining tasks. Pre-processing could be removing the anomalies and noise from the data that is about to be mined, filling in missing values, normalizing the data, or even compressing the data with the help of techniques like the aggregation and generalization.
Clustering: This is a partitioning with a massive set of data into the related subclasses.
Classification: This is a process of tagging or classifying data items into the different user-defined categories. Outlier analysis helps in the identification of those data elements, which are deviant or distant from the rest of the ingredients in a dataset. This can even help in anomaly detection.
The associative analysis helps in bringing out some of the hidden relationships among the data items in the broad set of data. This can even help in predicting the occurrence of a particular item in an event or transaction whenever some other things are present. You can also think of this as a conditional probability.
Regression is used to predict up with a dependent variable by constructing a model or a mathematical function out of indecent variables.
Summarization helps in coming up with a short description of the entire data sets. Data mining is integration and combination of various techniques, which include machine learning, statistics, pattern recognition, etc. while there is even a right amount of intersection between data mining and machine learning data, as both go together and algorithms of machine learning are also used for mining data.
There is no mystery why R is the superstar of free mining tools on this list. It is free, easy to pick up ad open-source with little or even no programming language experience. It runs on a wide range of popular Operating system platforms, which includes the mac OS, Windows, UNIX, and Linux. Some of the people have even referred R as the “Excel for the new modern generation.” There are, as of now, thousands of pre-built packages which is available for you to download so that you can start running the most advanced learning algorithms against the extensive data sets.
R is a powerful data mining tools because it even allows you to perform three different tasks all within just one platform:
Data Manipulation: Developers can also slice large multivariate datasets that are quickly, thus allowing for a format that is easy to digest and analyst.
Data Visualization: Once you have easily sliced your dataset, then you can use shelf graph functions in the R so that to visualize the data. This visualization also includes a wide range of animated and interactive graphs.
Data Analysis: R has around 4000 packages that perform the statistical analysis.
RapidMiner and R are most of the time at the top of their games in terms of usage and popularity. RapidMiner even tends to be a preferred choice for the next-gen “smart plan” manufacturers and startups. Mobile applications and chatbots also ted to depend on this software platform for rapid prototyping, app development, predictive data analysis, machine learning, and text mining for the customer experience.
RapidMiner is an open-source predictive analytics software that can be used at the time of getting started on any of the projects related to data mining. A free desktop version is also available, which even allows the use of four accelerators: Marketing, Churn, Sentiment Analysis, and Predictive Maintenance. You can even see use either the free sample data sets to walk away with this product or also swap the data with your own.
3. IBM SPSS Modeler
If you are also working on a broad set of scale on the projects like the textual analytics, then you will find the IBM SPSS workbench and its visual interface extremely. It even allows you to generate a wide range of data mining algorithms with having no knowledge of programming.
You would also use this for a large part of the anomaly detection, CARMA, Basic neutral networks, Cox Regression, and Bayesian networks that seven use the multi-layer perceptions with backpropagation learning.
This data mining tools can even be purchased with the help of a monthly subscription, and at the moment which they are also offering a 30 day free trial for those who are also much more interested in having a taste of how these predictive analytics can change the game of much more improved decision making.
4. SAS Data Mining
Turn to this tool quickly for the enterprise-level work, as users do not necessarily need much more statistical skills to generate the model with the help of a data mining tool. Utilizing the SAS Rapid Predictive Modeler, non-technical users are guided with the help of a set of data mining tasks.
It even captured the leading top right corner evaluations by the Gartner and Forrester, so that the investors will be on board.
SAS is also the right choice for predictive market models and dimension reduction techniques and creating interactive visualizations for better decision making and presentations. You can only have access to a limited free version of this software with the help of educational institutions.
If you do any of the contract work for a large organization that runs on the SAS enterprise, then make sure to take advantage of each and every moment.
As an open and free source language that can be easily downloaded and installed on your computer, Python is most often compared to the R for the more sue of feasibility. Just like the R, Python learning curve even tends to be so short it has become legendary.
Many of the users find that they can start building data sets and doing some of the extremely complex affinity analysis in minutes, which makes this a handy and efficient data mining tools. The most common business uses the case data visualizations are also straightforward as long as you are much more comfortable with some of the primary programming languages like the functions, variables, conditionals, loops, and data types.
If you are much new to the Python, then there are a wide range of books, as well as the reviews and tutorials that will help you to understand Python at an advanced level of learning.
A great example of what Python programming language can create, Orange is aa suite of software with the help of machine learning components and data manipulation processes. It ideal and free for beginners, coming with the help of multiple tutorials with preloaded with the data mining workflows.
Some of the most common visualizations needed for a professional career are just a few clicks away, which includes the heat maps, scatter plots, text mining, dendrograms. Orange even makes this list of best free data mining tools because of its super easy interactive visuals that can be easily made by anyone, advanced or even at the beginner level.
Advanced users of the Orange can also use it as a Python library for altering widgets and data manipulation. Orange even learns your preference as you even use it.
People who are already familiar with the database backgrounds are much more comfortable with the KNIME’s user-friendly framework. It built on the idea of modular data pipelining and interactive tables.
The name is short for the Konstanz Information Miner, that refers to the German university where it was born, this also tends to be the first level of choice of those in life sciences, who even sometimes extol the virtues of its intuitive graphical user interface.
For those who are just new to this data mining tool, KNIME has provided a series of short tutorials to understand better the data science and how to use the platform in a more effective and efficient way on the official website.
The attraction of the Spark is glowing into the market with the help of vast oceans of data traffic with ease. Spark jobs run with the help of a Python that is being deployed in the intensive data projects by everyone from NASA to Amazon. If you are even moving into a big data analytics or network edge or IOT career, then you will surely need to know about the Spark.
Eventually, one of the best open-source data mining tools to deal with the help of these massive amounts of data. Spark is a set apart from the other data mining tools because of its overall simplicity, speed, reliability, as well as its support of a large number of programming languages, which includes Java, Scala, R, and Python.
A spark which started in the year 2009 as a Project at the University of California at Berkeley within the lab of the AMPLab and is now taking a good share into the market as the usage of the top data mining tools.
It is even funded by some of the corporate investors such as Huawei, IBM, and Databricks. To have a much better and right understanding of the Spark, you can download the free eBook, which you can find anywhere on the web that shared you with all the several usages of the Spark.
If you want to get out on the latest cutting-edge technology efficiently only, start learning the H20. In its time period of just less than five years, it is installed thousands of times, with the applications for fraud detection at the PayPal and customer metrics for the popular WordPress plus Share This.
Like the R, it has a very active and enthusiastic user community that is even propelling its growth. H2O makes the list of the top data mining tools because of its accurate and fast in-memory processing of data large sets, its scalability with the big data, and its feasibility of use.
In the year 2018, was also recognized to add the leader among the 16 vendors described by the Gartner 2018 Magic Quadrant for the Machine Learning platform and Data Science. It is even used by companies like Capital One, Comcast, Cisco, Macy’s, TCS, IIBM, and ADP.
Business intelligence, data analytics, and data discovery are at the center of the Qlik’s platform. The vendor, which even touts the concepts of “democratized data,” provides powerful tools for handling with several of predictive analysis, science tasks, and data mining.
This platform is even available for both the cloud and offline versions. And both include the robust tools for building analytics apps and visualizations that click the Ai and Machine Learning to generate the recommendations and suggestions.
Qlik is designed for those who are even lacking the skills of data science. It also accommodates a wide range of data forms, which includes powerful data discovery capabilities and provides particularly with the strong geo-analytics capabilities. What is more, the vendor network of integrators and partners is extensive.
Qlik boasts around 2000 partners across the globe. Gartner revealed that it ranks the company “Leader” in its MQ. It is even praised by the company commitments to innovation and cited its active community of users as positives.
11. IBM Cognos Analytics
IBM is the most trusted company as of now, which came into the name of data science – which is a big plus for these data mining tools. Big Blue Flagship data product, Cognos Analytics, has even emerged to be the top contender in the world of Business Intelligence and data mining. It is also billed as an all-in-one-solution.
IBM Cognos Analytics delivers guided cognitive capabilities, automated predictive analytics, and data discovery. Data preparation functions, which include the: Ability to import the numerous file formats, which consists of the CSV files and spreadsheets. Data source search with the help of natural language processing and tools to simplify the verifying and combining data sources with the help of automated modeling.
Moreover, the platform also provides the visual data exploration tools, which include the smart visualization feature that even sometimes recommends the best format or chart and dashboards that offers with the precious and in-depth reporting capabilities as well. It also consists of the alerting functions and scheduling,
Tableau is a framework that promotes its data mining and business intelligence framework as a powerful and straightforward solution for non-data scientists. It also requires no technical expertise or coding experience.
The vendors provide an intuitive, visually oriented drag and drop interface that connects with the numerous data sources, which include the Google BigQuery, SAP, Salesforce, Amazon Redshift, AWS, and others.
Tableau provides its data science solution in three formats: Tableau Server, Tableau Online, and Tableau Desktop. The latter is a cloud-based solution. All support strong content governance, data preparation, data discovery, collaboration, and data access.
The platform even supports multiple devices, which include the PC, desktop, and laptop, with the help of a browser, smartphones, and embedded capabilities. Gartner also ranks the vendor a “Leader” and describes it as a “Gold Standard” for the visual exploration as an interactive. It even ranks near the top of the customer’s scores and overall satisfaction.
Weka is a Java-based open and free software licensed under the GNU GPL and available for the use of Mac OS, Linux, Unix, Windows. It even comprises a collection of machine learning algorithms for data mining. It packages tools for the classifications clustering, pre-processing, association rules, visualization, and regression. The wide range of accessing it are Weka Knowledge Explorer, Knowledge Flow, Experimenter, and Simple CL.
Explorer is a user-friendly graphical user interface for the 2D visualization of mined data. It even lets you import the raw data from the various file formats and also supports the well-known algorithms for the different mining actions like the attribute selection, clustering, classification, and filtering.
Moreover, when dealing with the large data sets, it is even best to use a CL based approach as an Explorer tries to load with the entire data sets into the main memory, which causes the performance issues.
This tool also offers a Java Appetizer for use in applications that can connect with the help o database using the CJD. Weka has proved to be the ideal choice for educational and research purposes as well as for rapid prototyping.
14. Dundas BI
Dundas BI is another top and best-rated analytics platform, which is known for its superb integrations and fast insights. The system even brings with the several analytics tools to allow the unlimited transformation of industry data and enriches the standard reporting with the appealing graphs, charts, and tables.
Another thing that you will appreciate about it is a graph free protection of your documents, as well as the significant possibility to access the data from any of the devices.
Dundas does more than merely analyzing your data: which even structures all pieces in a particular way to make the processing much easier for you and also links your charts and tables to help you quickly understand what that data means.
Thanks to the relational methods that you can even perform for your business. To make the matter even better, Dundas Business Intelligence provides help to reduce the corporate costs, as it also generates reliable reports, and even eliminates all need to use additional software.
Before you even make any of the final decisions on what is the right data mining tool for you, start from the end to end and work backward.
All the tools and software which are discussed above are so far not the only available ones over the market; the list keeps on growing on a day to day basis. While we have already covered lots of tools that are top and best, and that could aid in mining like the NLTK, Neural Designer, Pandas, SPMF, and Scikit-learn, which readers could explore easily. We will keep you updated on the data science latest news as well! Stay updated.