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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.
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps, thus "long short-term memory". It is applicable to classification, processing and predicting data based on time series, such as in handwriting, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare.
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works.
AMiner is a free online service used to index, search, and mine big scientific data.
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
Prabhakar Raghavan is a senior vice president at Google, where he is responsible for Google Search, Assistant, Geo, Ads, Commerce, and Payments products. His research spans algorithms, web search and databases. He is the co-author of the textbooks Randomized Algorithms with Rajeev Motwani and Introduction to Information Retrieval.
Usama M. Fayyad is an American-Jordanian data scientist and co-founder of KDD conferences and ACM SIGKDD association for Knowledge Discovery and Data Mining. He is a speaker on Business Analytics, Data Mining, Data Science, and Big Data. He recently left his role as the Chief Data Officer at Barclays Bank.
Anil Kumar Jain is an Indian-American computer scientist and University Distinguished Professor in the Department of Computer Science & Engineering at Michigan State University, known for his contributions in the fields of pattern recognition, computer vision and biometric recognition. He is among the top few most highly cited researchers in computer science and has received various high honors and recognitions from institutions such as ACM, IEEE, AAAS, IAPR, SPIE, the U.S. National Academy of Engineering, the Indian National Academy of Engineering and the Chinese Academy of Sciences.
Social media mining is the process of obtaining big data from user-generated content on social media sites and mobile apps in order to extract actionable patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research. The term is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to shift through vast quantities of raw ore to find the precious minerals; likewise, social media mining requires human data analysts and automated software programs to shift through massive amounts of raw social media data in order to discern patterns and trends relating to social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, and more. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs, new products, processes or services.
Gregory I. Piatetsky-Shapiro is a data scientist and the co-founder of the KDD conferences, and co-founder and past chair of the Association for Computing Machinery SIGKDD group for Knowledge Discovery, Data Mining and Data Science. He is the founder and president of KDnuggets, a discussion and learning website for Business Analytics, Data Mining and Data Science.
Larry Paul Heck is currently the Rhesa Screven Farmer, Jr., Advanced Computing Concepts Chair, Georgia Research Alliance Eminent Scholar, and professor at the Georgia Institute of Technology. His career spans many of the sub-disciplines of artificial intelligence, including conversational AI, speech recognition and speaker recognition, natural language processing, web search, online advertising and acoustics. He is probably best known for his role as the founder of the Microsoft Cortana Personal Assistant and his early work in deep learning for speech processing.
Geoffrey J. Gordon is a professor at the Machine Learning Department at Carnegie Mellon University in Pittsburgh and director of research at the Microsoft Montréal lab. He is known for his research in statistical relational learning and on anytime dynamic variants of the A* search algorithm. His research interests include multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of difficult data, computational learning theory, and game theory.
Gautam Das is a computer scientist in the field of databases research. He is an ACM Fellow and IEEE Fellow.
Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension.
Mi Zhang is a computer scientist at Ohio State University, where he is an Associate Professor of Computer Science and Engineering and the director of AIoT and Machine Learning Systems Lab. He is best known for his work in Edge AI, Artificial Intelligence of Things (AIoT), machine learning systems, and mobile health.
Yixin Chen is a computer scientist, academic, and author. He is a professor of computer science and engineering at Washington University in St. Louis.
Nitesh V. Chawla is a computer scientist and data scientist currently serving as the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame. He is the Founding Director of the Lucy Family Institute for Data & Society. Chawla's research expertise lies in machine learning, data science, and network science. He is also the co-founder of Aunalytics, a data science software and cloud computing company. Chawla is a Fellow of the American Association for the Advancement of Sciences (AAAS), Association for Computing Machinery (ACM), a Fellow of the Association for the Advancement of Artificial Intelligence, a Fellow of the Asia Pacific Artificial Intelligence Association, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has received multiple awards, including the 1st Source Bank Commercialization Award in 2017, Outstanding Teaching Award (twice), IEEE CIS Early Career Award, National Academy of Engineering New Faculty Award, and the IBM Big Data Award in 2013. One of Chawla's most recognized publications, with a citation count of over 25,000, is the research paper titled "SMOTE: Synthetic Minority Over-sampling Technique." Chawla's research has garnered a citation count of over 62,000 and an H-index of 80.
References
- ↑ "2012 elevated fellow" (PDF). IEEE Fellows Directory.
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