Data Mining And Business Intelligence (2170715)

Concept Hierarchy

• A concept hierarchy defines a sequence of mappings from a set of low-level concepts to higher-level, more general concepts

Types of concept hierarchy

1. 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.
2. Histogram analysis
   • Histogram is a popular data reduction technique
   • Divide data into buckets and store average (sum) for each bucket
   • Can be constructed optimally in one dimension using dynamic programming
   • Related to quantization problems.
3. Clustering analysis
   • Partition data set into clusters, and one can store cluster representation only
   • Can be very effective if data is clustered but not if data is “smeared”
   • Can have hierarchical clustering and be stored in multi-dimensional index tree structures
4. Entropy-based discretization
5. Segmentation by natural partitioning
   • 3-4-5 rule can be used to segment numeric data into relatively uniform, “natural” intervals.
   • If an interval covers 3, 6, 7 or 9 distinct values at the most significant digit, partition the range into 3 equi-width intervals
   • If it covers 2, 4, or 8 distinct values at the most significant digit, partition the range into 4 intervals
   • If it covers 1, 5, or 10 distinct values at the most significant digit, partition the range into 5 intervals