Thinking-data-science-a-data-science-practitioners-guide

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Contents
10 Connectivity-Based Clustering ............................ 185
Agglomerative Clustering ................................. 185
In a Nutshell ........................................ 185
The Working . . . . . . . . . . . . ............................ 185
Advantages and Disadvantages . . . . . ...................... 188
Applications ......................................... 189
Implementation . . . . . . . . . . . . . . . . . . . ................... 189
Project . . ............................................. 189
Divisive Clustering ...................................... 192
In a Nutshell ........................................ 192
The Working . . . . . . . . . . . . ............................ 192
Implementation Challenges . . . . .......................... 194
Summary ............................................. 194
11 Gaussian Mixture Model ................................. 197
In a Nutshell ........................................... 197
Gaussian Distribution .................................... 197
Probability Distribution ................................... 198
Selecting Number of Clusters . . . ............................ 200
Implementation ......................................... 200
Project . . ............................................. 200
Determining Optimal Number of Clusters ...................... 202
Summary ............................................. 207
12 Density-Based Clustering ................................ 209
DBSCAN ............................................. 209
In a Nutshell ........................................ 209
Why DBSCAN? . . .................................... 210
Preliminaries . ....................................... 211
Algorithm Working . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
Advantages and Disadvantages . . . . . ...................... 212
Implementation . . . . . . . . . . . . . . . . . . . ................... 213
Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
OPTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
In a Nutshell ........................................ 216
Core Distance . ....................................... 216
Reachability Distance .................................. 217
Implementation . . . . . . . . . . . . . . . . . . . ................... 218
Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
Mean Shift Clustering .................................... 222
In a Nutshell ........................................ 222
Algorithm Working . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Implementation . . . . . . . . . . . . . . . . . . . ................... 225
Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Summary ............................................. 228