Formation of higher dimensional subspace clusters

3 views (last 30 days)
How to find higher dimensional clusters are formed by connecting 2-dimensional subspace clusters sharing common objects. If an outlier object is by chance becomes part of a one dimensional cluster, it will be absent in clusters present in remaining dimensions and its support will be below attribute_threshold. Such outlier objects get eliminated in this step. The attributes containing 2-dimensional clusters are arranged in non-decreasing order on the basis of percentage of coverage of the data items belonging to the clusters. The objects in a 2-dimensional cluster in an attribute are connected to objects in next attribute in the sequence if they contain common object indices to form higher dimensional subspace clusters. And find the count of dimensions in a subspace cluster is less than attribute_threshold.The default attribute threshold is set as 5.I want to do that in Z.
clc;
clear;
data=xlsread('Glassxl.xlsx');
asc=sort(data);
minpts=4;
epsilon=2;
tic
idx=dbscan(asc,epsilon,minpts);
figure(1)
gscatter(asc(:,1),asc(:,2),idx);
title('DBSCAN Using Euclidean Distance Metric')
Z = linkage(idx,'ward');
How can I find the and merge those clusters that share a common object.How can I do that please help me.

Accepted Answer

Hiro Yoshino
Hiro Yoshino on 28 Jan 2020
I can be your help though, I don't quite get you.
Let me ask you: do you have a good understanding on DBSCAN in the first place?
  8 Comments
Hiro Yoshino
Hiro Yoshino on 30 Jan 2020
Good to know.
Please acept the answer and close this question. Good luck.

Sign in to comment.

More Answers (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!