Big Data Mining

It is different from the process of statistics and analysis; data mining is a calculation which mainly based on the existing data to conduct different arithmetic. Moreover, its prediction will be helped in realizing some senior data requirements.

Tools Introduction
RapidMiner 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. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results in visualization, model validation, and optimization.
WEKA Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Orange Orange is often a component of structured data mining as well as a machine learning software suite created in the python language. It's a good open-source data visualization as well as evaluation about novice and experts. Data mining can be done via visual programming or even python scripting. Its components concerning machine learning.
NLTK NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
KNIME KNIME is an open-source data analytics, reporting, and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface allows assembly of nodes for data preprocessing (ETL: Extraction, Transformation, Loading), for modeling and data analysis and visualization without, or with only minimal, programming.