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Gregory Piatetsky-Shapiro in NYC. Gregory I. Piatetsky-Shapiro (born 7 April 1958) 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. [1]
He served as the director of the Stanford Artificial Intelligence Laboratory (SAIL), where he taught students and undertook research related to data mining, big data, and machine learning. His machine learning course CS229 at Stanford is the most popular course offered on campus with over 1,000 students enrolling some years.
Widely regarded as the father of modern machine data processing, his invention of the punched card tabulating machine marked the beginning of the era of semiautomatic data processing systems 1986 Hopcroft, John: Fundamental achievements in the design and analysis of algorithms and data structures 1952 Hopper, Grace
Jiawei Han – data mining; Frank Harary – graph theory; Brian Harris – machine translation research, Canada's first computer-assisted translation course, natural translation theory, community interpreting (Critical Link) Juris Hartmanis – computational complexity theory; Johan Håstad – computational complexity theory
Prof. Joseph Weizenbaum, computer critic Kevin Warwick, cyborg scientist, implant self-experimenter; Niklaus Wirth, developed Pascal; Peter J. Weinberger, co-developer of the AWK language
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Anomaly detection benchmark data repository with carefully chosen data sets of the Ludwig-Maximilians-Universität München; Mirror Archived 2022-03-31 at the Wayback Machine at University of São Paulo. ODDS – ODDS: A large collection of publicly available outlier detection datasets with ground truth in different domains.
If δ ≤ Rejection Region, the data point is not an outlier. The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier). Meaning, if a data point is found to be an outlier, it is removed from the data set and the test is applied again with a new average and rejection region.