A Modular Spatial Clustering Algorithm with Noise Specification
Akhil K Srikanth H R
Clustering techniques have been the key drivers of data mining, machine
learning and pattern recognition for decades. One of the most popular
clustering algorithms is DBSCAN due to its high accuracy and noise tolerance.
Many superior algorithms such as DBSCAN have input parameters that are hard to
estimate. Therefore, finding those parameters is a time consuming process. In
this paper, we propose a novel clustering algorithm Bacteria-Farm, which
balances the performance and ease of finding the optimal parameters for
clustering. Bacteria- Farm algorithm is inspired by the growth of bacteria in
closed experimental farms - their ability to consume food and grow - which
closely represents the ideal cluster growth desired in clustering algorithms.
In addition, the algorithm features a modular design to allow the creation of
versions of the algorithm for specific tasks / distributions of data. In
contrast with other clustering algorithms, our algorithm also has a provision
to specify the amount of noise to be excluded during clustering.