Autonomous clustering by fast find of mass and distance peaks
Clustering is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. We propose a novel clustering algorithm based on the natural idea that a cluster and its nearest neighbor with higher mass should be merged into one cluster, unless they both have relatively large masses and the distance between them is also relatively large. The find of mass and distance peaks reveals the mergers that don’t conform to the rule and should be removed. The algorithm is parameter-free and harnesses this idea to recognize any cluster and find the proper number of clusters and noise autonomously. Experiments on numerous synthetic and real-world data sets show the enormous versatility of the proposed algorithm that remarkably outperforms the best compared algorithm. Additionally, we also compare it with latest state-of-the-art deep clustering algorithms on several challenging image data sets. The proposed algorithm without any deep representation achieves better or close performance than deep clustering algorithms on image clustering.
Code (1)
Tasks
AstronomyClusteringDeep ClusteringImage ClusteringSimilar Papers 제목 키워드 기반
Long-Range LiDAR Vehicle Detection Through Clustering and Classification for Autonomous Racing
With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existi…
Autonomous DrivingAutonomous RacingClusteringComputational Efficiency+2Clustering via torque balance with mass and distance
Grouping similar objects is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. Inspired by the torque balance that exists in gravitationa…
AstronomyClusteringSDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets
This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume that all the data is memory-resident, o…
ClusteringOutlier DetectionEfficient Clustering with Limited Distance Information
Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one vers…
ClusteringRock the KASBA: Blazingly Fast and Accurate Time Series Clustering
Time series data has become increasingly prevalent across numerous domains, driving a growing demand for time series machine learning techniques. Among these, time series clustering (TSCL) stands out as one of the most p…
Anomaly DetectionClusteringTime SeriesTime Series Clustering