Density

Let’s try density on data from k-means example: > dens1<-density(aggtime) > dens1 Call: density.default(x = aggtime) Data: aggtime (14 obs.); Bandwidth 'bw' = 10.25 x y Min. :-29.855 Min. :3.287e-05 1st Qu.: 1.298 1st Qu.:2.239e-03 Median : 32.450...

Anomaly detection

Very important data mining (data analytical) pattern finding method. It is the basic method for intrusion / fraud detection and system health check. These all there areas needs to know about anomalies which are very different from other data points. Very simple...

Hierarchical clustering

Clusters are in hierarchy – smaller cluster(s) is (are) part of bigger cluster and so on. There are two methods how to achieve this. Logically they are “from the top to the bottom” and vice versa. Implementation in R: “hclust” Clusters...

K-medoids clustering

Is similar to “k-means” clustering. But does not create new points as centroids. Instead uses existing data points and tries to find “k” centroids among them. At the start randomly chooses “k” data points and computes distance of...