LINSCAN -- A Linearity Based Clustering Algorithm

TL;DR

LINSCAN is a novel clustering algorithm that detects lineated clusters by embedding points as distributions and using KL divergence, effectively identifying intersecting faults in seismic data.

Contribution

The paper introduces LINSCAN, a new clustering method that leverages distribution embeddings and KL divergence to find complex lineated clusters.

Findings

LINSCAN successfully identifies intersecting faults in seismic data.

It distinguishes spatially close but orthogonally oriented clusters.

The paper discusses stability properties for generalized density-based clustering algorithms.

Abstract

DBSCAN and OPTICS are powerful algorithms for identifying clusters of points in domains where few assumptions can be made about the structure of the data. In this paper, we leverage these strengths and introduce a new algorithm, LINSCAN, designed to seek lineated clusters that are difficult to find and isolate with existing methods. In particular, by embedding points as normal distributions approximating their local neighborhoods and leveraging a distance function derived from the Kullback Leibler Divergence, LINSCAN can detect and distinguish lineated clusters that are spatially close but have orthogonal covariances. We demonstrate how LINSCAN can be applied to seismic data to identify active faults, including intersecting faults, and determine their orientation. Finally, we discuss the properties a generalization of DBSCAN and OPTICS must have in order to retain the stability benefits of these algorithms.