Evolutionary Clustering via Message Passing
- URL: http://arxiv.org/abs/1912.11970v1
- Date: Fri, 27 Dec 2019 03:09:16 GMT
- Title: Evolutionary Clustering via Message Passing
- Authors: Natalia M. Arzeno, Haris Vikalo
- Abstract summary: We introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph.
EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots.
EAP determines the number of clusters and tracks them automatically.
- Score: 21.366647840787365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We are often interested in clustering objects that evolve over time and
identifying solutions to the clustering problem for every time step.
Evolutionary clustering provides insight into cluster evolution and temporal
changes in cluster memberships while enabling performance superior to that
achieved by independently clustering data collected at different time points.
In this paper we introduce evolutionary affinity propagation (EAP), an
evolutionary clustering algorithm that groups data points by exchanging
messages on a factor graph. EAP promotes temporal smoothness of the solution to
clustering time-evolving data by linking the nodes of the factor graph that are
associated with adjacent data snapshots, and introduces consensus nodes to
enable cluster tracking and identification of cluster births and deaths. Unlike
existing evolutionary clustering methods that require additional processing to
approximate the number of clusters or match them across time, EAP determines
the number of clusters and tracks them automatically. A comparison with
existing methods on simulated and experimental data demonstrates effectiveness
of the proposed EAP algorithm.
Related papers
- Clustering Based on Density Propagation and Subcluster Merging [92.15924057172195]
We propose a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space.
Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process.
arXiv Detail & Related papers (2024-11-04T04:09:36Z) - A3S: A General Active Clustering Method with Pairwise Constraints [66.74627463101837]
A3S features strategic active clustering adjustment on the initial cluster result, which is obtained by an adaptive clustering algorithm.
In extensive experiments across diverse real-world datasets, A3S achieves desired results with significantly fewer human queries.
arXiv Detail & Related papers (2024-07-14T13:37:03Z) - Incremental Affinity Propagation based on Cluster Consolidation and
Stratification [2.048226951354646]
We propose A-Posteriori affinity Propagation (APP) to achieve faithfulness and forgetfulness.
APP enforces incremental clustering where i) new arriving objects are dynamically consolidated into previous clusters without the need to re-execute clustering over the entire dataset of objects.
Experimental results show that APP achieves comparable clustering performance while enforcing scalability at the same time.
arXiv Detail & Related papers (2024-01-25T14:20:00Z) - Reinforcement Graph Clustering with Unknown Cluster Number [91.4861135742095]
We propose a new deep graph clustering method termed Reinforcement Graph Clustering.
In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework.
In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters.
arXiv Detail & Related papers (2023-08-13T18:12:28Z) - Clustering individuals based on multivariate EMA time-series data [2.0824228840987447]
Ecological Momentary Assessment (EMA) methodological advancements have offered new opportunities to collect time-intensive, repeated and intra-individual measurements.
Advanced machine learning (ML) methods are needed to understand data characteristics and uncover meaningful relationships regarding the underlying complex psychological processes.
arXiv Detail & Related papers (2022-12-02T13:33:36Z) - Mitigating shortage of labeled data using clustering-based active
learning with diversity exploration [3.312798619476657]
We propose a clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling.
A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes.
arXiv Detail & Related papers (2022-07-06T20:53:28Z) - Self-supervised Contrastive Attributed Graph Clustering [110.52694943592974]
We propose a novel attributed graph clustering network, namely Self-supervised Contrastive Attributed Graph Clustering (SCAGC)
In SCAGC, by leveraging inaccurate clustering labels, a self-supervised contrastive loss, are designed for node representation learning.
For the OOS nodes, SCAGC can directly calculate their clustering labels.
arXiv Detail & Related papers (2021-10-15T03:25:28Z) - You Never Cluster Alone [150.94921340034688]
We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation.
We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one.
By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
arXiv Detail & Related papers (2021-06-03T14:59:59Z) - A New Parallel Adaptive Clustering and its Application to Streaming Data [0.0]
This paper presents a parallel adaptive clustering (PAC) algorithm to automatically classify data while simultaneously choosing a suitable number of classes.
We develop regularized set mik-means to efficiently cluster the results from the parallel threads.
We provide theoretical analysis and numerical experiments to characterize the performance of the method.
arXiv Detail & Related papers (2021-04-06T17:18:56Z) - Dynamic Clustering in Federated Learning [15.37652170495055]
We propose a three-phased data clustering algorithm, namely: generative adversarial network-based clustering, cluster calibration, and cluster division.
Our algorithm improves the performance of forecasting models, including cellular network handover, by 43%.
arXiv Detail & Related papers (2020-12-07T15:30:07Z) - LSD-C: Linearly Separable Deep Clusters [145.89790963544314]
We present LSD-C, a novel method to identify clusters in an unlabeled dataset.
Our method draws inspiration from recent semi-supervised learning practice and proposes to combine our clustering algorithm with self-supervised pretraining and strong data augmentation.
We show that our approach significantly outperforms competitors on popular public image benchmarks including CIFAR 10/100, STL 10 and MNIST, as well as the document classification dataset Reuters 10K.
arXiv Detail & Related papers (2020-06-17T17:58:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.