Incremental Affinity Propagation Based on Cluster Consolidation and Stratification

Information bottleneck based incremental fuzzy clustering for large biomedical data

An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data

Affinity Propagation is a clustering algorithm used in many applications. It iteratively updates messages between data points until convergence. The message updating process enables Affinity Propagation to have higher clustering quality compared with other approaches. However, its computation cost is high; it is quadratic in the number of data points. This is because it updates the messages of all data point pairs. This paper proposes an efficient algorithm that guarantees the same clustering results as the original algorithm. Our approach, F-AP, is based on two ideas: (1) it computes upper and lower estimates to limit the messages to be updated in each iteration, and (2) it dynamically detects converged messages to efficiently skip unneeded updates. Experiments show that F-AP is much faster than previous approaches with no loss in clustering performance.

Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation [7] to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.

Supervised Adaptive Incremental Clustering for data stream of chunks

Urban transportation systems encounter significant challenges in extracting meaningful traffic patterns from extensive historical datasets, a critical aspect of smart city initiatives. This paper addresses the challenge of analyzing and understanding these patterns by employing various clustering techniques on hourly urban traffic flow data. The principal aim is to develop a model that can effectively analyze temporal patterns in urban traffic, uncovering underlying trends and factors influencing traffic flow, which are essential for optimizing smart city infrastructure. To achieve this, we applied DBSCAN, K-Means, Affinity Propagation, Mean Shift, and Gaussian Mixture clustering techniques to the traffic dataset of Aarhus, Denmark's second-largest city. The performance of these clustering methods was evaluated using the Silhouette Score and Dunn Index, with DBSCAN emerging as the most effective algorithm in terms of cluster quality and computational efficiency. The study also compares the training times of the algorithms, revealing that DBSCAN, K-Means, and Gaussian Mixture offer faster training times, while Affinity Propagation and Mean Shift are more computationally intensive. The results demonstrate that DBSCAN not only provides superior clustering performance but also operates efficiently, making it an ideal choice for analyzing urban traffic patterns in large datasets. This research emphasizes the importance of selecting appropriate clustering techniques for effective traffic analysis and management within smart city frameworks, thereby contributing to more efficient urban planning and infrastructure development.

Clustering data into meaningful subsets is a major task in scientific data analysis. To date, various strategies ranging from model-based approaches to data-driven schemes, have been devised for efficient and accurate clustering. One important class of clustering methods that is of a particular interest is the class of exemplar-based approaches. This interest primarily stems from the amount of compressed information encoded in these exemplars that effectively reflect the major characteristics of the respective clusters. Affinity propagation (AP) has proven to be a powerful exemplar-based approach that refines the set of optimal exemplars by iterative pairwise message updates. However, a critical limitation is its inability to capitalize on known networked relations between data points often available for various scientific datasets. To mitigate this shortcoming, we propose geometric-AP, a novel clustering algorithm that effectively extends AP to take advantage of the network topology. Geometric-AP obeys network constraints and uses max-sum belief propagation to leverage the available network topology for generating smooth clusters over the network. Extensive performance assessment reveals a significant enhancement in the quality of the clustering results when compared to benchmark clustering schemes. Especially, we demonstrate that geometric-AP performs extremely well even in cases where the original AP fails drastically.

An autocorrelation incremental fuzzy clustering framework based on dynamic conditional scoring model

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

APSCAN: A parameter free algorithm for clustering

The affinity propagation algorithm is a distance-based clustering algorithm, which has the advantages of fast clustering speed, high accuracy, and no need to specify the number of clusters. The quantum particles swarm optimization algorithm has powerful parameter optimization capabilities. In this paper, we use the quantum particles swarm optimization algorithm, named QPSO-SAP, to optimize the bias parameters. Experiments show that the improved AP algorithm improves the clustering performance significantly on complex structured data sets.

In high-dimensional gene co-expression network analysis, capturing the temporal changes of gene associations is crucial for unveiling dynamic regulatory mechanisms inherent in biological systems. Examining how these interactions change over time offers valuable insights into the developmental and adaptive processes that drive an organism’s lifecycle. Moreover, incorporating structural prior information can substantially enhance the accuracy and interpretability of the estimated sparse dynamic gene network. Methods previously proposed in the literature cannot simultaneously model sparse time-varying co-expression network structure and have the power-law degree distribution. Additionally, there is a demand of time-efficient, memory-light software implementations and possibility to utilize repeated measures at each time-point (if available). In this paper, we introduce the time-varying scale-free graphical lasso (tvsfglasso), a novel scalable framework for estimating high-dimensional time-varying gene co-expression networks under the assumption that these networks simultaneously exhibit sparse and a scale-free structure. We utilize fast algorithms developed for the graphical lasso (glasso), which makes tvsfglasso a scalable tool for high-dimensional problems. We evaluate the performance of tvsfglasso using both simulated and real-world dynamic gene expression time series datasets, demonstrating its capability to detect temporal changes in gene associations. Our results highlight the potential of tvsfglasso to advance the understanding of dynamic gene networks, making this estimator useful for more accurate modeling of complex biological processes.

Several clustering algorithms have been extensively used to analyze vast amounts of spatial data. One of these algorithms is the SNN (Shared Nearest Neighbor), a density-based algorithm, which has several advantages when analyzing this type of data due to its ability of identifying clusters of different shapes, sizes and densities, as well as the capability to deal with noise. Having into account that data are usually progressively collected as time passes, incremental clustering approaches are required when there is the need to update the clustering results as new data become available. This paper proposes SNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> , an incremental clustering algorithm based on the SNN. Its performance and the quality of the resulting clusters are compared with the SNN and the results show that the SNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sup> yields the same result as the SNN and show that the incremental feature was added to the SNN without any computational penalty. Moreover, the experimental results also show that processing huge amounts of data using increments considerably decreases the number of distances that need to be computed to identify the points' nearest neighbors.

Tandem mass spectrometry (MS/MS) has become the analytical backbone of large-scale untargeted metabolomics, routinely generating millions of spectra per study. However, existing clustering methods struggle to process this scale due to computational and memory bottlenecks, limiting the utility of clustering in downstream analysis. This bottleneck is especially acute in long-term studies and public repositories, where new data are continuously added over time. Here we present a scalable clustering framework for MS/MS metabolomics data. Our method incrementally incorporates new spectra batches while preserving clustering performance through a novel spectrum pooling strategy, which propagates local density structure across batches. Using both database-search-based evaluation on proteomics data sets and the MS1-retention time (MS-RT) method on metabolomics data sets, we show that incremental clustering achieves comparable performance to the state-of-the-art clustering methods in terms of cluster purity and completeness. Critically, our approach scales up to clustering tasks consisting of 368 million spectra clustering task and millions of clusters, completing in under 10,000 CPU hours, while traditional methods could not scale to this data volume and failed to complete due to excessive memory or time requirements. Our method offers a practical solution for large-scale, continuously growing MS/MS studies and is well suited for integration into public metabolomics platforms such as GNPS2.

Clustering sensor nodes into groups is an efficient topology control approach for achieving long-term operation of Wireless Sensor Networks (WSNs). The performance of clustering is greatly influenced by the selection of Cluster Heads (CHs), which are in charge of creating clusters and controlling member nodes. Finding the optimal set of CHs is known to be non-deterministic polynomial (NP)-hard problem for a WSN. Evolutionary computation approaches can be applied to find fast and efficient solutions to such problems. In this paper, the problem of CHs election is formulated as a single-objective optimization problem, aiming to obtain clusters that maximize the network energy efficiency and link quality. The formulated problem has been solved using three Evolutionary approaches: Genetic Algorithms (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO). Their performance has been compared in terms of the achieved fitness value. In addition, the performance of the proposed protocol is evaluated and compared to well-known cluster-based sensor network protocols.