Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications
Clustering techniques play an important role in data mining and its related applications. Among the challenging applications that require robust and real-time processing are crowd management and group trajectory applications. In this paper, a robust and low-complexity clustering algorithm is proposed. It is capable of processing data in a manner that is shape and centroid independent. The algorithm is of low complexity due to the novel technique to compute the matrix power. The algorithm was tested on real and synthetic data and test results are reported.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringManagementSimilar Papers 제목 키워드 기반
CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance
This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids…
ClusteringImage Clustering without Ground Truth
Cluster analysis has become one of the most exercised research areas over the past few decades in computer science. As a consequence, numerous clustering algorithms have already been developed to find appropriate partiti…
ClusteringClustering EnsembleImage ClusteringMultiview Representation Learning from Crowdsourced Triplet Comparisons
Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the question ``among three given objects, which two…
AttributeRepresentation LearningTripletCPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation
Nucleus segmentation is a challenging task due to the crowded distribution and blurry boundaries of nuclei. Recent approaches represent nuclei by means of polygons to differentiate between touching and overlapping nuclei…
SegmentationImbalanced Data Clustering using Equilibrium K-Means
Centroid-based clustering algorithms, such as hard K-means (HKM) and fuzzy K-means (FKM), have suffered from learning bias towards large clusters. Their centroids tend to be crowded in large clusters, compromising perfor…
ClusteringDeep Clustering