Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. In 2011, authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date".
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative rapid qualitative data analysis and interactive data visualization.
Neural network software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks, and in some cases, a wider array of adaptive systems such as artificial intelligence and machine learning.
Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data.
KNIME, the Konstanz Information Miner, is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing, for modeling, data analysis and visualization without, or with only minimal, programming.
Pentaho is business intelligence (BI) software that provides data integration, OLAP services, reporting, information dashboards, data mining and extract, transform, load (ETL) capabilities. Its headquarters are in Orlando, Florida. Pentaho was acquired by Hitachi Data Systems in 2015 and in 2017 became part of Hitachi Vantara.
ELKI is a data mining software framework developed for use in research and teaching. It was originally at the database systems research unit of Professor Hans-Peter Kriegel at the Ludwig Maximilian University of Munich, Germany, and now continued at the Technical University of Dortmund, Germany. It aims at allowing the development and evaluation of advanced data mining algorithms and their interaction with database index structures.
Feature Selection Toolbox (FST) is software primarily for feature selection in the machine learning domain, written in C++, developed at the Institute of Information Theory and Automation (UTIA), of the Czech Academy of Sciences.
Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.
Ian H. Witten is a computer scientist at the University of Waikato, New Zealand. He is a Chartered Engineer with the Institute of Electrical Engineers in London who graduated from the University of Cambridge with a BA and MA in mathematics in 1969 and an M.Sc. in mathematics and computer science from the University of Calgary, where he was a Commonwealth Scholar, in 1970. He received his Ph.D. for Learning to Control in 1976 from the University of Essex, England. Witten discovered temporal-difference learning, inventing the tabular TD(0), the first temporal-difference learning rule for reinforcement learning. Witten is a co-creator of the Sequitur algorithm and conceived and obtained funding for the development of the original WEKA software package for data mining. Witten further made considerable contributions to the field of compression, creating novel algorithms for text and image compression with Alistair Moffat and Timothy C. Bell. He is also one of the major contributors to the digital libraries field, and founder of the Greenstone Digital Library Software.
Feature engineering or feature extraction or feature discovery is the process of using domain knowledge to extract features from raw data. The motivation is to use these extra features to improve the quality of results from a machine learning process, compared with supplying only the raw data to the machine learning process.
Neural Designer is a software tool for machine learning based on neural networks, a main area of artificial intelligence research, and contains a graphical user interface which simplifies data entry and interpretation of results.
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are learned.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning. The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.
scikit-mutliflow is a free and open source software machine learning library for multi-output/multi-label and stream data written in Python.
