What is the concept of hierarchy in data mining?
Data mining systems should provide users with the flexibility to tailor predefined hierarchies according to their particular needs. Concept hierarchies may also be defined by discretizing or grouping values for a given dimension or attribute, resulting in a set-grouping hierarchy.
What is concept of hierarchy?
A hierarchy is an organizational structure in which items are ranked according to levels of importance. Most governments, corporations and organized religions are hierarchical.
How are concept hierarchies useful in OLAP explain?
Concept hierarchies organize the values of attributes or dimensions into abstraction levels. They are useful in mining at multiple abstraction levels. Typical OLAP operations include roll-up, and drill-( down, across, through), slice-and-dice, and pivot ( rotate), as well as some statistical operations.
What are data mining functionalities?
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. Data mining tasks can be classified into two categories: descriptive and predictive. Descriptive mining tasks characterize the general properties of the data in the database.
What is concept hierarchy list and briefly explain types of concept hierarchy?
List and explain types of concept hierarchy. Concept Hierarchy reduce the data by collecting and replacing low level concepts (such as numeric values for the attribute age) by higher level concepts (such as young, middle-aged, or senior).
Why are hierarchies important in data warehouses?
In data warehouse systems, the hierarchies play a key role in processing and monitoring information. Through these operations we can get summarized as well as detailed data which aids in analysis as well as decision making process.
What are the types of hierarchy?
Main Types of Hierarchical Organization
- Bureaucratic or orthodox organization.
- Professional organization.
- Representative democratic organization.
- Hybrid or postmodern organization.
What are the types of concept hierarchies?
Types of concept hierarchy
- Binning. In binning, first sort data and partition into (equi-depth) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.
- Histogram analysis.
- Clustering analysis.
- Entropy-based discretization.
- Segmentation by natural partitioning.
What are the two categories of data mining functionality?
Data mining operations are divided into two types, which are descriptive and predictive. Descriptive mining tasks describe the general characteristics of the database’s data. Predictive mining tasks produce predictions by making inferences on current data.
What are the four data mining techniques?
In this post, we’ll cover four data mining techniques: Regression (predictive) Association Rule Discovery (descriptive) Classification (predictive)
Can you generate a concept hierarchy for categorical attributes?
Without knowledge of data semantics, a concept hierarchy can be automatically generated based on the number of distinct values in attributes at the lowest level of hierarchy; the higher levels include gen- erated concepts in the hierarchy.
What is the concept hierarchy for location dimension?
dimension has 4-level concept hierarchy of country, state, city and area/word id as shown in Fig.
Does concept hierarchy matter in data mining?
As one of the most important background knowledge, concept hierarchy plays a fundamentally important role in data mining. It is the purpose of this thesis to study some aspects of concept hierarchy such as the automatic generation and encoding technique in the context of data mining.
How do you generate a concept hierarchy in a database?
Concept hierarchies may be provided manually by system users, domain experts, or knowledge engineers, or may be automatically generated based on statistical analysis of the data distribution. The automatic generation of concept hierarchies is discussed in Chapter 3 as a preprocessing step in preparation for data mining.
What is an ageconcept hierarchy and how is it used?
Concept hierarchies can be used to reduce the data y collecting and replacing low-level concepts (such as numeric value for the attribute age) by higher level concepts (such as young, middle-aged, or senior).
What is data discretization and concept hierarchy generation?
Data Discretization and Concept Hierarchy Generation. – Bottom-up. starts by considering all of the continuous values as potential split- points, removes some by merging neighborhood values to form intervals, and then recursively applies this process to the resulting intervals.