APSCAN: A parameter free algorithm for clustering

Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected.

Portfolio optimization refers to the reasonable allocation of assets to achieve the investment objectives. At present, the investment environment depression was due to sluggish economic conditions. For investors, they expect to find a balance between return and risk in a complex investment environment. In order to solve this problem, this paper proposes a portfolio optimization algorithm named Portfolio Optimization based on Affinity propagation and Genetic algorithm, as also called POGA, which based on affinity propagation and genetic algorithm. Firstly, the affinity propagation algorithm is used to construct a candidate set of portfolio based on the correlation analysis of the stock time series. Secondly, using the Sharpe-ratio as the Optimization objective function, the genetic algorithm is used to solve an optimal portfolio strategy with higher-return and lower-risk. Finally, the experimental result of real-world stock data show that a portfolio with higher return and lower risk will be selected.

Biometric identification is getting more and more popular as the demand of security increases. Iris is a promising biometric because of its stability and uniqueness. In this paper, a novel iris analysis method is proposed by using Affinity Propagation (AP) Algorithm. AP algorithm is a new clustering method by transforming the input matrix of similarity between pairs of data points. The proposed method is evaluated using the Chinese Academy of Sciences-Institute of Automation (CASIA) iris image database, the similarity between two irises is measured by their negative Hamming Distance. Experiments indicate that the algorithm has excellent solution in iris analysis, when AP algorithm is used as preprocess in an iris identification system, it can greatly decrease the time complexity of iris recognition.

<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging problems: unreasonable division of data gridding, poor parameter optimization ability and low efficiency of parallelization. In this study, a density-based clustering algorithm by using improve fruit fly optimization based on MapReduce (MR-DBIFOA) is proposed to tackle these three problems. Firstly, based on KD-Tree, a division strategy (KDG) is proposed to divide the cell of grid adaptively. Secondly, an improve fruit fly optimization algorithm (IFOA) which use the step strategy based on knowledge learn (KLSS) and the clustering criterion function (CFF) is designed. In addition, based on IFOA algorithm, the optimal parameters of local clustering are dynamically selected, which can improve the clustering effect of local clustering. Meanwhile, in order to improve the parallel efficiency, the density-based clustering algorithm using IFOA (MR-QRMEC) are proposed to parallel compute the local clusters of clustering algorithm. Finally, based on QR-Tree and MapReduce, a cluster merging algorithm (MR-QRMEC) is proposed to get the result of clustering algorithm more quickly, which improve the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MR-DBIFOA algorithm has better clustering results and performs better parallelization in big data.</p> <p> </p>

Affinity Propagation is one of the fundamental clustering algorithms used in various Web-based systems and applications. Although Affinity Propagation finds highly accurate clusters, it is computationally expensive to apply Affinity Propagation to a large dataset since it requires iterative computations for all possible pairs of data objects in the dataset. To address the aforementioned issue, this paper presents efficient Affinity Propagation algorithms, namely \textit{C-AP}. In order to increase the clustering speed, C-AP employs \textit{cell-based index} to reduce the number of the computed data object pairs in the clustering procedure. By using the cell-based index, C-AP efficiently detects unnecessary pairs, which do not contribute to its clustering result. For further reducing the computation time, we also present an extension of our algorithm named \textit{Parallel C-AP} that utilizes thread-parallelization techniques. As a result, C-AP and Parallel C-AP detects the same clusters as those of Affinity Propagation with much shorter computation time. Extensive evaluations demonstrate the performance superiority of our proposed algorithms over the state-of-the-art algorithms.

With the proliferation of smartphones and wearable devices having Micro-Electro-Mechanical Systems (MEMS) sensors built in, data samples of linear acceleration and angular velocity can be collected almost anytime anywhere. These motion data can be used to identify various types of human motions and to detect the anomaly of individuals movements. This work presents attempts to use the unsupervised Affinity Propagation (AP) clustering algorithm and the supervised Support Vector Machine (SVM) classification algorithm to identify four types of human gait motions: walking, jogging, climbing upstairs and downstairs. Features of three-dimensional linear acceleration that can enable the algorithms to identify these motion types correctly were selected by analyzing the variation of the feature values among different motion types. Efficacy of Affinity Propagation (AP), Linear and Non-linear Support Vector Machine (SVM) algorithms were also studied by comparing their ratios of correct, false positive, false negative and F1 score classification. This preliminary study demonstrated Linear SVM achieved the best performance, followed by Affinity Propagation. Quite surprisingly, Non-linear SVM appeared to be inferior to the other two algorithms.

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP--hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

While density based clustering algorithms are able to detect clusters of arbitrary shapes, their clustering results usually rely heavily on some user-specified parameters. In order to solve this problem, in this paper we propose to combine dominant sets and density based clustering to obtain reliable clustering results. We firstly use the dominant sets algorithm and histogram equalization to generate the initial clusters, which are usually subsets of the real clusters. In the second step the initial clusters are extended with density based clustering algorithms, where the required parameters are determined based on the initial clusters. By merging the merits of both algorithms, our approach is able to generate clusters of arbitrary shapes without user-specified parameters as input. In experiments our algorithm is shown to perform better than or comparably to some state-of-the-art algorithms with careful parameter tuning.

<div class="section abstract"><div class="htmlview paragraph">Driver steering feature clustering aims to understand driver behavior and the decision-making process through the analysis of driver steering data. It seeks to comprehend various steering characteristics exhibited by drivers, providing valuable insights into road safety, driver assistance systems, and traffic management. The primary objective of this study is to thoroughly explore the practical applications of various clustering algorithms in processing driver steering data and to compare their performance and applicability. In this paper, principal component analysis was employed to reduce the dimension of the selected steering feature parameters. Subsequently, K-means, fuzzy C-means, the density-based spatial clustering algorithm, and other algorithms were used for clustering analysis, and finally, the Calinski-Harabasz index was employed to evaluate the clustering results. Furthermore, the driver steering features were categorized into lateral and longitudinal categories. Different clustering algorithms were selected for clustering analysis to assess potential differences between lateral and longitudinal steering features and to determine the applicability of various algorithms for this purpose. In the comparative analysis, we comprehensively evaluate the performance of various algorithms in terms of clustering quality, interpretability, computational efficiency, and applicability. Selecting algorithms suitable for specific tasks and datasets is crucial to enhancing computational efficiency and the quality of clustering results. Through the comparative analysis of the driver steering feature clustering algorithm, a deeper understanding of the steering behavior mode can be obtained, providing support for optimizing the driver assistance system, advancing automatic driving technology, enhancing the driving experience, optimizing traffic management, and ultimately realizing a more intelligent and safer road traffic system.</div></div>

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

This paper presents a clustering algorithm based on Voronoi diagrams. The algorithm firstly constructs irregular grids in plane by Voronoi diagrams, then assign the points among different grids to different clusters according to the property of the Voronoi diagrams' “the nearest neighbor”. It is able to automatically modify the final clustering number based on the grid points' density, and it can adjust the locations for the Voronoi's seeds by the changes of the centroids, and the final Voronoi cells becomes the clustering result. The algorithm is able to settle down the clustering numbers automatically and also can recognize the low density points automatically. The experiments prove that the algorithm can cluster effectively the data points in plane, and its performance is similar to the X-means algorithm which is improved on the K-means algorithm. It is more effective than the DBSCAN and the OPTICS which are density-based clustering algorithms. The algorithm proved to be obviously more effective while the experimental data is in a larger scale.

Our research is based is to create implementations of two common clustering algorithms K-Means and Affinity Propagation. There are certain drawbacks of K-Means however the main drawback with this and the same kind of similar algorithms is in selecting some number of clusters, and choosing the initial set of points. Affinity Propagation locates exemplars between datum or data points or series points and thus forms clusters of this datum around these exemplars. After forming the groups it operates by simultaneously considering all d atum as probable exemplars and interchange messages between data points till a good set of exemplars and clusters appears. Finally, we have finish making an Affinity Propagation program which nearly supervene the details laid out because the affinity propagation has an aptness to form many clusters. Using these two programs we slandered a variety of one and two dimensional datasets, and we examined the results to confirm that Affinity Propagation produces clustering errors in involving competition with that of K-Means in an order of enormity less time.

Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

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.

To provide services during large-scale natural disasters, it is crucial for network operators to have adaptive and intelligent solutions. With this in mind, new solutions need to be developed, as conventional ground base stations (GBSs) may not be suitable or fast enough to provide services in such emergency situations. Hence, a research gap for Emergency Communications Networks (ECNs) is the deployment of unmanned aerial vehicles (UAVs) in emergency areas. To address this research gap, this paper focuses on the efficient and optimal three-dimensional deployment of UAVs in scenarios characterized by high user density and heterogeneous distributions. The purpose of this study is to address real-world challenges, including the spatial distribution of users and the simultaneous presence of multiple GBSs. This study attempted to develop a novel data clustering approach for wireless networks, based on the Affinity Propagation algorithm, referred to as Deep-Embedded Affinity Propagation Clustering (DEAPC). By integrating deep learning to map data into a latent feature space, this method enhances the clustering algorithm’s capability to handle scattered and noisy data. Moreover, a novel mechanism is designed to accommodate the presence of multiple GBS and to assign users to them. Simulation outcomes demonstrate that the new approach outperforms current leading clustering algorithms, reducing the required number of UAVs whereas also increasing system sumrate and decreasing computational time. This research presents a new method for creating intelligent, resilient, and adaptive UAV-based wireless networks in disaster scenarios.