KDD
In plain wordsThe leading yearly conference on data mining — finding useful patterns in large datasets — where many widely used machine learning tools were first presented.
The ACM SIGKDD Conference on Knowledge Discovery and Data Mining, the premier venue for data miningdata mining, n.: Searching large collections of data with computers to discover useful patterns, such as products often bought together or signs that a transaction may be fraudulent. and applied data science, held each August. It emphasizes scalable algorithms and real-world applications — recommendationrecommender system, n.: Software that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on., fraud detection, web and social-network analysis, advertising and health — and runs a research track alongside an applied data science track for industry papers. Influential KDD papers include the DBSCAN clustering algorithm (1996) and, in 2016, XGBoostXGBoost, prop. n.: A fast, free software tool that makes predictions by adding up hundreds of small decision trees, each one fixing the mistakes of those before it. For years it was behind many winning entries in competitions on spreadsheet-style data., node2vec and the LIMELIME, abbr.: A method that explains one prediction of any AI model by testing slightly altered versions of the input and fitting a very simple model that copies the AI's behavior near that example. explanation method; its KDD Cup, held since 1997, was one of the first recurring machine learning competitions.
Origin
Grew out of workshops on knowledge discovery in databases organized by Gregory Piatetsky-Shapiro from 1989; the first full conference met in Montreal in 1995, and the ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), founded in 1998, took over its organization.
Related
- XGBoostXGBoost, prop. n.: A fast, free software tool that makes predictions by adding up hundreds of small decision trees, each one fixing the mistakes of those before it. For years it was behind many winning entries in competitions on spreadsheet-style data.
- recommender systemrecommender system, n.: Software that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on.
- LIMELIME, abbr.: A method that explains one prediction of any AI model by testing slightly altered versions of the input and fitting a very simple model that copies the AI's behavior near that example.
- ICMLICML, prop. n.: A leading machine learning conference, first held as a workshop in 1980, where researchers gather each summer to present new learning methods and theory.
- NeurIPSNeurIPS, prop. n.: The largest yearly scientific conference on artificial intelligence and machine learning, held each December, where many of the field's landmark papers were first presented.