Relational data mining


Relational data mining is the data mining technique for relational
databases. Unlike traditional data mining algorithms, which look for
patterns in a single table,
relational data mining algorithms look for patterns among multiple tables
. For most types of propositional
patterns, there are corresponding relational patterns. For example,
there are relational classification rules, relational regression tree, and relational association rules.
There are several approaches to relational data mining:
  1. Inductive Logic Programming
  2. Statistical Relational Learning
  3. Graph Mining
  4. Propositionalization
  5. Multi-view learning

    Algorithms

Multi-Relation Association Rules: Multi-Relation Association Rules is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules, each rule item consists of one entity but several relations. These relations indicate indirect relationship between the entities. Consider the following MRAR where the first item consists of three relations live in, nearby and humid: “Those who live in a place which is near by a city with humid climate type and also are younger than 20 -> their health condition is good”. Such association rules are extractable from RDBMS data or semantic web data.

Software