For the rules in DMQL
Syntax:
Generalization:
generalize data [into (relation_name)]
Association:
find association rules [as (rule_name)]
Classification:
find classification rules [as (rule_name) ] according to [(attribute)]
Characterization:
find characteristic rules [as (rule_name)]
Discrimination:
find discriminant rules [as (rule_name)] for (class_1) with (condition_1) from (relation(s)_1) in contrast to (class_2) with (condition_2) from (relation(s)_2) { in contrast to (class_i) with (condition_i) from (relation(s)_i)}
Data Mining Query Language
Data Mining is a process is in which user data are extracted and processed from a heap of unprocessed raw data. By aggregating these datasets into a summarized format, many problems arising in finance, marketing, and many other fields can be solved. In the modern world with enormous data, Data Mining is one of the growing fields of technology that acts as an application in many industries we depend on in our life. Many developments and researches have been held in this field and many systems are also been disclosed. Since there are numerous processes and functions to be done in Data Mining, a very well developed user interface is needed. Even though there are many well-developed user interfaces for the relational systems, Han, Fu, Wang, et al. proposed the Data Mining Query Language(DMQL) to further build more developmental systems and innovate many kinds of research in this field. Though we can’t consider DMQL as a standard language. It is a derived language that stands as a general query language to perform data mining techniques. DMQL is executed in DB miner systems for collecting data from several layers of databases.