0

8.3kviews

Explain data discretization and concept hierarchy generation

**1 Answer**

0

8.3kviews

Explain data discretization and concept hierarchy generation

1

729views

written 9 months ago by | • modified 9 months ago |

- Dividing the range of a continuous attribute into intervals.
- Interval labels can then be used to replace actual data values.
- Reduce the number of values for a given continuous attribute.
- Some classification algorithms only accept categorically attributes.
- This leads to a concise, easy-to-use, knowledge-level representation of mining results.
- Discretization techniques can be categorized based on whether it uses class information or not such as follows:
This discretization process uses class information.*Supervised Discretization -*This discretization process does not use class information.*Unsupervised Discretization -*

- Discretization techniques can be categorized based on which direction it proceeds as follows:

**Top-down Discretization -**

- If the process starts by first finding one or a few points called split points or cut points to split the entire attribute range and then repeat this recursively on the resulting intervals.

**Bottom-up Discretization -**

- 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.

- Discretization can be performed rapidly on an attribute to provide a hierarchical partitioning of the attribute values, known as a
*Concept Hierarchy.* - Concept hierarchies can be used to reduce the data by collecting and replacing low-level concepts with higher-level concepts.
- In the multidimensional model, data are organized into multiple dimensions, and each dimension contains multiple levels of abstraction defined by concept hierarchies.
- This organization provides users with the flexibility to view data from different perspectives.
- Data mining on a reduced data set means fewer input and output operations and is more efficient than mining on a larger data set.
- Because of these benefits, discretization techniques and concept hierarchies are typically applied before data mining, rather than during mining.

**1] Binning**

- Binning is a top-down splitting technique based on a specified number of bins.
- Binning is an unsupervised discretization technique because it does not use class information.
- In this, The sorted values are distributed into several buckets or bins and then replaced with each bin value by the bin mean or median.
- It is further classified into
*Equal-width (distance) partitioning**Equal-depth (frequency) partitioning*

**2] Histogram Analysis**

- It is an unsupervised discretization technique because histogram analysis does not use class information.
- Histograms partition the values for an attribute into disjoint ranges called buckets.
- It is also further classified into
*Equal-width histogram**Equal frequency histogram*

- The histogram analysis algorithm can be applied recursively to each partition to automatically generate a multilevel concept hierarchy, with the procedure terminating once a pre-specified number of concept levels has been reached.

**3] Cluster Analysis**

- Cluster analysis is a popular data discretization method.
- A clustering algorithm can be applied to discretize a numerical attribute of A by partitioning the values of A into clusters or groups.
- Clustering considers the distribution of A, as well as the closeness of data points, and therefore can produce high-quality discretization results.
- Each initial cluster or partition may be further decomposed into several subcultures, forming a lower level of the hierarchy.

**4] Entropy-Based Discretization**

- Entropy-based discretization is a supervised, top-down splitting technique.
- It explores class distribution information in its calculation and determination of split points.
- Let D consist of data instances defined by a set of attributes and a class-label attribute.
- The class-label attribute provides the class information per instance.
- In this, the interval boundaries or split-points defined may help to improve classification accuracy.
- The entropy and information gain measures are used for decision tree induction.

**5] Interval Merge by χ2 Analysis**

- It is a bottom-up method.
- Find the best neighboring intervals and merge them to form larger intervals recursively.
- The method is supervised in that it uses class information.
- ChiMerge treats intervals as discrete categories.
- The basic notion is that for accurate discretization, the relative class frequencies should be fairly consistent within an interval.
- Therefore, if two adjacent intervals have a very similar distribution of classes, then the intervals can be merged.
- Otherwise, they should remain separate.

ADD COMMENT
EDIT

Please log in to add an answer.