| | Category | SOFT | P27 | An Integrated Approach to Novel TSP-Based Clustering |
| | Algorithms |
| | Abstract | Clustering is a concept that involves creating or finding structure within |
| | unstructured data. Given an arbitrary set of data points, clustering |
| | algorithms have the goal of organizing these points into sets, with |
| | different algorithms trying to accomplish different tasks. DBSCAN and |
| | K-mean clustering are the two most common, with DBSCAN clustering |
| | attempting to find clusters based on radial distance from core points, |
| | and K-mean clustering forming sets using the least distances to the K- |
| | center. Main issues with clustering algorithms include timeliness as |
| | many are NP-hard and the arbitrary nature of the data set and |
| | conditions make many variations of algorithms. |
| | The traveling salesman problem is a problem where a salesman must |
| | travel to all the cities presented exactly once and arrives back at his |
| | starting point. The solution is the path that is of the least length, and |
| | this type of linear optimization problem is considered NP-hard. |
| | For my project, I have combined the ideas of TSP and clustering |
| | algorithms. Using Java, I wrote a program implementing the greedy |
| | algorithm, specifically nearest neighbor, of constructing TSP solutions. |
| | That method is combined with the DBSCAN method of clustering |
| | algorithms to find the number of clusters that will be formed given a |
| | maximum tour. The K-means clustering algorithm is modified to find the |
| | optimal method of clustering data points to minimize the average tour |
| | length. These methods are proven to work and compared to the status |
| | quo using random data, and empirical is given to prove the superiority. |
| | |
| | This algorithm is very useful in fields involving shipping and networking, |
| | in which there may be one agent for a cluster of clients, so it would be |
| | useful in finding how to assign such agents to clients. Additionally, I |
| | have programmed it so that it is very easily modifiable and can be used |
| | in multi-dimensional analysis. |
| | Bibliography | https://sites.google.com/site/dataclusteringalgorithms/density-based- |
| | clustering-algorithmhttps://www.naftaliharris.com/blog/visualizing-k- |
| | means-clustering/ |