Do you want to extract important information from available data or turn raw data into useful information? These top best data mining tools help you examine raw data, improve your business’s performance, and develop better marketing strategies.
What is Data Mining?
Data mining is the act of transforming a large set of data into effective information. Data mining’s major purpose is to discover various patterns between large sets of data and convert it into more actionable and refined information.
This method uses database systems, artificial intelligence, statistical analysis, and specific algorithms. Its purpose is to extract information from many datasets and transform it into an understandable structure for future use.
Top Best Data Mining Tools
1. Rapid Miner
Rapid Miner is a data mining tool that offers integrated featuresof predictive analysis, text mining, deep learning, machine learning, and data preparation. Rapid Miner is among the highest leading open-source systems for data mining. It’s entirely written in Java programming language.
Oracle Data Mining is used by several market-leading organizations to maximize the potential of their data and to make specific/accurate predictions. Oracle Data Mining is one of Oracle’s Advanced Analytics Database representatives and functions with a powerful data algorithm that helps target the best clients. This data mining tool helps identifies both cross-selling and anomalies opportunities and helps you use a different predictive model according to your need. Also, it designs customer profiles in the desired manner.
IBM SPSS Modeler is a data mining tool used for text analytics and data mining to build predictive models. IBM SPPS Modeler was initially built by SPPS Inc. and was subsequently owned by IBM.
The tool possesses a visual interface to enable you to work with mining algorithms without programming. IBM SPSS Modeler eradicates the unwanted difficulties experienced during data conversion to produce easy-to-use predictive models.
KNIME is also known as Konstanz Information Miner. This data mining tool is an open-source data analysis platform that helps you familiarize, scale, and deploy data in less time. In business, the tool is regarded as the platform that enables inexperienced users to access predictive intelligence. It also helps uncover data potential. KNIME has a variety of integrated tools/algorithms.
This data mining tool is available as a free/open-source language. The tool is usually compared to R for ease of use. However, its learning curve seems so short that it becomes easy to use. Several persons discover that they can carry out complex affinity analysis with Python and begin building datasets in minutes.The most popular business-use case-data visualizations are straightforward as far as you are familiar with simple programming principles such as loops, conditionals, functions, data types, and variables.
Orange is an alternative to Oracle Advance Analytics. It is a data mining tool that offers a great data mining algorithm for specialized analytics, regression, prediction, and data classification that helps analysts detect fraud, identify cross-selling opportunities, target best customers, make better predictions, and analyze insights.
The algorithms customized within ODM leverage Oracle database’s potential strength. SQL’s data mining feature helps dig data out of schemas, views, and database tables.
Oracle’s data miner’s GUI is an extended version of Oracle SQL Developer. It offers a drag-and-drop data feature within the database to users, therefore offering great insight.
Teradata acts as an enterprise data warehouse. It is also known as Teradata database and consists of data mining software and data management tools. Teradata is used for business analytics and can create insight into company data, such as customer preference, product placement, and sales.
The data mining tool helps you differentiate between ‘cold’ and ‘hot’ data. This implies that Teradata puts data that isn’t frequently used in a slow storage section. Because it possesses server nodes with their own processing ability and memory, it functions on ‘share nothing’ architecture.
This data mining tool is a free and open software package that offers a graphical user interface for mining data using the R statistical programming language Togaware offers. The tool offers the feature of considerable data mining by exposing the power of the R via a graphical user interface. You can use Rattle as a teaching facility to learn the R. and option known as ‘Log Code tab’ helps you clone the R code any activity (that can be copied and pasted) the GUI undertakes.
You can use Rattle to generate model or analyze statistics. It enables you to partition the dataset into testing, validation, and training.
Another name for Weka is Waiko Environment. Weka is a machine learning software that is developed at the University of Waiko, New Zealand. Weka is better used for predictive modeling and data analysis. Weka consists of a visualization tool and algorithms that support machine learning. It possesses a GUI that promotes easy access to all its functionalities. Weka is written in Java programming language.
It supports specific data mining, such as regression, visualization, processing, and data mining. Weka functions on the belief that data is available in flat-file format. It offers SQL Database access via database connectivity. It helps you process the results and data the query returns.
SQL Server Data Tools is a declarative, universal model that carries out the expansion of all database development phases in the Visual Studio IDE. The transact of SQL Server Data Tools is used by developers to refactor, debug, maintain, and build databases.
With SSDT, you can directly work with a connected database or a database, therefore offering off and on-premise facilities. It enables you to use visual studio to develop databases such as programming support, code navigation tools, and IntelliSense through visual basic, C#, etc. SQL Server Data Tools offers Table Designer that helps you create new tables and edit tables in connected and direct databases.
11. Apache Mahout
Apache Foundation is responsible for the development of Apache Mahout. Apache aimsto create machine learning algorithms. The tool primarily focuses on collaborative filtering, classification, and data clustering.
The tool is written in JAVA libraries to carry out mathematical activities such as statistics and linear algebra. Mahout’s algorithms have implemented a level above Hadoop via reducing and mapping templates.
12. IBM Cognos
This data mining tool is an intelligence suite. IBM owns it for score carding, data analysis, and report, etc. IBM Cognos contains sub-components that meet specific organizational needs such as Workspace Advance, Event Studio, Analysis Studio, Report Studio, Query Studio, and Cognos Connection.
13. SAS Data Mining
SAS Data Mining is a SAS Institute’s product developed for data management and analytics. It can mine, alter, and manage data from various sources and carry out statistical analysis. SAS Data Mining offers graphical UI for non-technical users.
The tool allows you to large data and gets accurate insights to make specific decisions. It possesses a distributed memory processing architecture that is highly scalable. SAS is better used for optimization, text mining, and data mining.
This data mining software is commonly called Board toolkit. Board is a tool used for corporate performance management, analytics, and Business Intelligence. It is great for organizations desiring to improve decision making. It collects data from all the sources streamline them to create reports in the desired format.
The tool offers features of tracking performance planning, control workflows and perform multi-dimensional analysis.
Another name for DataMelt is DMelt. It is a visualization and computation environment that offers an interactive framework to carry out visualization and data analysis. DataMelt is primarily created for students, scientists, and engineers. It is written in JAVA language.
You can run DataMelt on any OS that is compatible with Java Virtual Machine (JVM). DataMelt consists of mathematical and scientific libraries. You can use it for statistical analysis, data mining, and large data volume analysis. DataMelt is commonly used in engineering, natural sciences, and financial markets analysis.
Before you finally decideon the data mining tool to purchase, you must examine the business requirement. You should consider various questions such as ‘Will the tool offer some value-adds that I have never experienced before?’‘Is the tool compatible with management and system?’‘Does it aim to increase efficiency?’‘Does it meet customer behavior?’
You should consider all these questions. Only when you have deeply thought about them and found suitable answers can you make a specific decision.