Factorization in experiment generation

TL;DR: This article explains What is data mining?

TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].

TL;DR: It is shown that heuristic methods for learning from larger scenes are likely to give an accurate hypothesis if they produce a simple hypothesis consistent with a large enough random sample and that this class of concepts is polynomiaIIy learnable from random examples in the sense of Valiant.

TL;DR: The version space approach has been implemented as one component of the Meta-DENDRAL program for learning production rules in the domain of chemical spectroscopy and proofs are given for the correctness of the method for representing version spaces, and of the associated concept learning algorithm, for any countably infinite concept description language.

TL;DR: This chapter focuses on the issue of learning heuristics to guide a forward-search problem solver, and describes a computer program called lex, which acquires problem-solving Heuristics in the domain of symbolic integration.

TL;DR: Sabidussi gives a non-algorithmic proof that the cartesian factorization is unique by using a tower of successively coarser equivalence relations on the edge set in which each prime factor of the graph is identified with an equivalence class in the coarsest of the relations.

TL;DR: It is proved that directed graphs have unique prime factorizations under cartesian multiplication and that the prime factorization of weakly connected digraphs in polynomial time is found.