Abstract :
Summary form only given, as follows. Data Mining, or Knowledge Discovery in Databases as it is also called, is claimed as an offspring by three disciplines: databases, statistics, and the machine learning subfield of artificial intelligence. (The term originated in statistics with distinctly pejorative overtones - data mining was characterized as fossicking in data without a guiding model.) Statistics is obviously relevant because that field has always focused on construction of models from data. Databases, too, is clearly central because current applications of data mining can involve very large corpora of information that are not necessarily in flat file form. So what??s left to be claimed by artificial intelligence and, in particular, machine learning? This talk will provide a definitely non-impartial answer to this question from the standpoint of a long-time ML practitioner.
