Intro

This is a course project of Data Mining (2024, fall) in THU.

Objective

Implementing clustering on the human activity dataset.

Data

6 kinds of activities with 561 features https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones

Content

1. data analysis and preprocessing

2. clustering analysis

2.1 algorithms

2.1.1 Partitioning Methods

• K-means
• CLARANS

2.1.2 Hierarchical Clustering Methods

• Agglomerative Hierarchical Clustering
• Divisive Hierarchical Clustering

2.1.3 Density-Based Clustering Methods

• DBSCAN

2.1.4 Grid-Based Clustering Methods

2.2 evaluation

2.2.1 clustering tendency

• Hopkins statistic

2.2.2 clustering quality

• Internal metrics
  • Compatness
  • Separation
  • lhouette score
  • Calinski-Harabasz index (CH index)
  • avies-Bouldin index (DB index)
• External metrics
  • Adjusted Rand Index (ARI)
  • Normalized Mutual Information (NMI)
  • V-Measure