2019-01-09[Stay Sharp]Clustering

作者: 三千雨点 | 来源:发表于2019-01-09 22:22 被阅读2次

2019-01-09[Stay Sharp]Clustering
面对无果的实相，如何努力而不麻木倦怠
要与什么在一以让自己保持敏锐
[Stay Sharp]Perceptron
[Stay Sharp]SVM基础
[Stay Sharp]SVM Python实现
[Stay Sharp]Gaussian Naive Bayes
[Stay Sharp]Naive Bayes Algorith
[Stay Sharp]Bayes Classifier Typ
[Stay Sharp]线性回归代码实现

Validation Approaches

Popular validation approaches involve Internal evaluation, External evaluation, Manual evaluation and Indirect evaluation.

Internal evaluation

Internal evaluation means the clustering result is evaluated based on the data that was clustered itself. With this evaluation, we get the algorithm, which produces clusters with high similarity within a cluster and low similarity between clusters. Also, this evaluation is biased towards algorithms that use the same cluster model.
the following methods are some popular internal evaluation methods:

Davies-Bouldin index (DB index or DBI).

$D B = \frac { 1 } { n } \sum _ { i = 1 } ^ { n } \max _ { j \neq i } \left( \frac { \sigma _ { i } + \sigma _ { j } } { d \left( c _ { i } , c _ { j } \right) } \right)$
where $n$ is the number of clusters, $c_{x}$ is the centroid of cluster $x$ , $\sigma _{x}$ is the average distance of all elements in cluster $x$ to centroid $c_{x}$ , and $d(c_{i},c_{j})$ is the distance between centroids $c_{i}$ and $c_{j}$ .

Dunn index (DI)

$D = \frac { \min _ { 1 \leq i < j \leq n } d ( i , j ) } { \max _ { 1 \leq k \leq n } d ^ { \prime } ( k ) }$

External evaluation

$TP$ is the number of true positives, $TN$ is the number of true negatives, $FP$ is the number of false positives, and $FN$ is the number of false negatives.