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Top 10 Data Mining Tools

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According to experts, data is considered to be priceless. However, it is not cost-effective or easy to analyze. With data witnessing exponential growth, a process will be needed to extract valuable information. Patterns of data are performed in large sets to convert it to effective information, termed as Data mining. This particular technique uses statistical analysis, specific algorithms, database systems & artificial intelligence to derive information from the huge datasets. It is then converted to understandable form. You need to be aware of the different Data Mining Tools to use.

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Data Analysis software list

#1 Oracle Data Mining

Oracle Data Mining

It is considered to be the representative of the Advanced Analytics Database of Oracle. Such Data Mining Tools are used to optimize their data potentiality to come up with accurate predictions. It has a powerful data algorithm and targets the best customers. Moreover, it identifies cross-selling opportunities & anomalies, thus enabling users to avail varied predictive models, depending on their needs.

#2 RapidMiner

RapidMiner data mining tool

This Data Analysis software offers an integrated environment for machine learning, data preparation, predictive analysis, text mining & deep learning. It is also one of the Apex open-source leading systems, written in Java language. There are arbitrarily nestable operators in huge numbers that are available in XML detailed files. They are created using a rapid miner’s graphical user interface.

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#3 Knime

Knime Data Mining Tool

It stands for Konstanz Information Miner & is regarded to be an open-source data analysis platform. You have the facility to deploy, familiarize & scale data quickly. Such Data Mining Tools can help make access predictive intelligence to inexperienced users. It also uncovers data potentiality and includes ready-to-use examples & modules in thousands.

#4 IBM SPSS Modeler

IBM SPSS Modeler logo

This Data Analysis software best fits large scale projects. Its sophisticated visual interface & text analytics are valuable. You can develop data mining algorithms without requiring much or any programming knowledge. It is used widely in Bayesian networks, anomaly detection, basic neural networks, Cox regression & CARMA.

#5 Orange

Orange Data Mining Tool

Such Data Mining Tools are considered to be a toolkit, machine learning & data visualization. It provides visual programming front-end to allow interactive data visualization and exploratory data analysis. This visual programming package is component-based on machine learning, data visualization, data analysis & mining.

#6 Python

python logo

This open-source language is available for free and due to its ease of use is compared with R. But its learning curve is found to be short and hence, easy to use. It is also easy to develop datasets and carry out complex affinity analysis quickly.

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#7 Rattle

togaware logo

This free & open Data Analysis software offers a graphical user interface. It uses Togaware’s R statistical program language. It offers significant data mining functionality. It exposes R’s power through a graphical user interface. It also allows partitioning of the dataset to test, validate, and train.

#8 Kaggle

Kaggle logo

It is considered to be the largest machine learner & data scientist community in the world. Originally, it offered machine learning competitions. However, it has extended presently to public cloud-based data-science platforms. Such Data Mining Tools allows solve difficult problems, to accentuate data science power & to recruit strong teams.

#9 Weka (Waikato Environment for Knowledge Analysis)

Weka logo

This machine learning Data Analysis software was developed at Waikato University, New Zealand. Java program is used for its development. It contains algorithms and visualization tools for predictive modeling and data analysis including a graphical user interface.

#10 Teradata

teradata logo

This analytical platform delivers the very best functions including leading engines. It allows user to optimize their language & tools, across diverse data types. It is achieved through embedding analytics near data & eliminating data movement. This in turn allows users to perform analytics with greater accuracy and speed against large datasets.

The above are the top Data Mining Tools that are being used in 2020.

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