1. A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence
Yue Ming
NJIT#:

2. About my project

3. Clustering algorithms, one important and normal used part of unsupervised machine learning methods, aim to discover the natural groupings of patterns, points, or objects. They divide a given dataset into multiple classes according to data similarity. In this work, I choose three clustering algorithms, k-MWO, AP clustering and DBSCAN. k-MWO clustering algorithm combines mussels wandering optimization (MWO), a heuristic method based on swarm intelligence, with classic k-means clustering algorithm. Affinity propagation (AP) takes as input measures of similarity between pairs of data points. DBSCAN is a density based algorithm for dealing with arbitrary shapes. 

4. Simulation & Result

5. Dataset 1 2-dimension. 2 classes. Each class has 1000 elements.
Conform to Gaussian distribution.

6. Initial data

7. After using K-MWO

8. Dataset 2 2-dimension. 2 classes. Arbitrary cluster ship.
1000 elements total.

9. Initial data

10. After using K-MWO

11. Analyze Dataset 1 Dataset 2 Algorithm time 12.536s 7.025s
Cluster accuracy rate
90.6%
45.2%

12. Performance Comparison
Dataset 1 (Arbitrary cluster ship with 1000 points)
Dataset 2 (Gaussian Distribution with 2000 points)
DBSCAN
K-MWO AP
Running Time
2.906s
7.025s
37.638s
Cluster Number 2 24
Accuracy Rate
99.6%
45.2%
9.76%
DBSCAN
K-MWO AP
Running Time
5.864s
12.536s
67.274s
Cluster Number 1 2
Accuracy Rate
42.7%
90.6%
89.3%

13. Conclusion
This paper presents a new clustering algorithm based on a new swarm intelligence model, called mussels wandering optimization (MWO).
It makes full use of the global optimization ability of MWO and the local search ability of K-means.
We test K-MWO, DBSCAN, AP algorithm in 2 same dataset.