![]() This framework focus is on three areas : engines recommended, clustering and classification. It is an open-source platform over APACHE Hadoop. ![]() Developers can mine data in Python and non-programmers can use drag and drop interface. ![]() It allows interactive data visualization, numerous types of graphs such as Silhouette plots and sieve diagrams. Molecular biologists and bioinformaticians can also use its extended capabilities. It is component-based software with a large collection of pre-built ML algorithms and text add-ons for mining. Open-source data science toolbox to develop, test and visualize mining workflows. It can be integrated with API and available in all major programming languages and uses distributed memory computing to analyse huge data sets in an efficient manner. It has support for common ML algorithms and has auto ML functions so users can build and deploy ML models in AI environments in a faster and simpler manner. It is open-source machine learning which aims to make AI accessible to everyone. Common use cases are credit scores, fraud detection and risk assessment of credits. It has a powerful set of instructions and integrations which makes it versatile and scalable to process complex data sets and use advanced sets of algorithms. It has different pre-built components to enable fast modelling which is code less. It has an intuitive interface to create end to end data science workflows, starting from modelling to production. It is a free open-source platform for machine learning and data mining. It was originally designed to analyse data in the field of agriculture but it is now used by researchers, industrial scientists and educational institutions. It has built machine learning algorithms to test ideas and deploy models without writing long codes. Variety of data mining tasks such as pre-processing, classification, regression, clustering, visualisation in GUI interface is supported. It is an open-source machine learning application developed by University of Waikato in New Zealand written in JavaScript having a vast collection of data mining algorithms. Standard version of tool supports numerical data from spreadsheets and relational databases, for analytical capabilities premium version is required. Data science teams can import vast amounts of data from various sources and arrange them to discover trends and patterns. Advanced algorithms can be used without much programming experience to build predictive models using drag and drop interfaces. It allows data scientists to visualize and speed up the data mining process. Java API can be used by developers to integrate models to discover new trends and patterns in business intelligence applications. It helps to build models which help to predict customer behaviour, segmentation of customer profiles, detection of fraud, and targeted best prospects. It has several data mining algorithms for activities like classification, regression, anomaly detection and prediction. It is a component of Oracle Advanced Analytics which enables data analysis to build and implement predictive models. RapidMiner studio helps to visualize results to spot patterns, outliers and data trends. It is a drag drop interface and pre-built modules to allow non-programmers to create intuitive workflows for specific user cases like fraud detections and customer churn. It is a free open-source data science platform which has hundreds of algorithms for data preparation, machine learning, deep learning, text mining and predictive analysis. Text mining tools are used to automate ticket tagging and routing in customer support, detect negative feedback in social media, and fine grained insight which lead to better decision making. It supports various data mining tasks such as detecting topics, sentiment, and intent to extract keywords and named entities. It is available in a user-friendly interface and can be integrated with existing tools to perform real time data mining. It is a machine learning platform which specializes in mining of text. List of top Data Mining Tools MonkeyLearn – In this article we will look at some data mining tools which made their place in the top 10 and shaped the IT and business landscape in the year 2021. ![]() It is an advanced analytics technique which combines machine learning and Artificial intelligence to get useful information which help businesses to know more about customer needs, improve revenues, reduce costs, enhance customer relationships etc. Data miningprocess helps to identify patterns and relationships in large data sets.
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