A Modular Spatial Clustering Algorithm with Noise Specification

TL;DR

This paper introduces Bacteria-Farm, a modular clustering algorithm inspired by bacterial growth, which balances performance with ease of parameter estimation and includes noise exclusion capabilities.

Contribution

The paper presents a novel, modular clustering algorithm that simplifies parameter tuning and explicitly handles noise, inspired by biological bacterial growth processes.

Findings

Balances clustering performance and parameter estimation ease

Includes explicit noise exclusion feature

Modular design for task-specific adaptations

Abstract

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.