Data Mining Query Language (DMQL) – For Knowledge Discovery
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In this Data Mining Tutorial, we will study Data Mining Query Language. As we will study every concept related to Query Language in Data Mining (DMQL), First we will study Query Language, Data Mining Query Language motivation and design. Further, will learn the syntax for every task and specification. Moreover, we will cover standardization and Query Language purposes.
2. Introduction to Data Mining Query Language
3. Motivation to Data Mining Query Language
4. Syntax of Data Mining Query Language
Syntax of DMQL for specifying task-relevant data
use database database_name
use data warehouse data_warehouse_name
in relevance to att_or_dim_list
from relation(s)/cube(s) [where condition]
order by order_list
group by grouping_list
5. Syntax – Specifying Kind of Knowledge
a. Data Mining Characterization
b. Data Mining Discrimination
c. Data Mining Association
d. Data Mining Classification
e. Data Mining Prediction
6. Syntax – Concept Hierarchy Specification
7. Syntax – Interestingness Measures Specification
8. Syntax – Pattern Presentation & Visualization Specification
9. Data Mining Languages Standardization
- Basically, it helps the systematic development of data mining solutions.
- Also, improves interoperability among multiple data mining systems and functions.
- Generally, it helps in Promoting education and rapid learning.
- Also, promotes the use of data mining systems in industry and society.
10. Purposes – Data Mining Query
Data Mining Queries are useful for many purposes are:
- Apply the model to new data, to make single or multiple predictions. You can provide input values as parameters, or in a batch.
- Get a statistical summary of the data used for training.
- Extract patterns and rule of the typical case representing a pattern in the model.
- Extract regression formulas and other calculations that explain patterns.
- Get the cases that fit a particular pattern.
- Retrieve details about individual cases used in the model. Also, it includes data not used in an analysis.
- Retrain a model by adding new data, or perform cross-prediction.
As a result, we have studied Data mining Query Language for Knowledge Discovery and it’s all related concepts. Also, we have covered all syntax’s specification and standardization along with Query Language purposes. I hope this blog will help you to understand the concept of Data Mining Query Language (DMQL). Furthermore, if you feel any query feel free to ask in a comment section.
See Also- Clustering in Data Mining & Data Mining Techniques