Automatic Clustering Algorithms Research Articles

Optimized data driven fault detection and diagnosis in chemical processes

Fault detection and diagnosis (FDD) is crucial for ensuring process safety and product quality in the chemical industry. Despite the large amounts of process data recorded and stored in chemical plants, most of them are not well-labeled, and their conditions are not adequately specified. In this study, an optimized data-driven FDD model was developed for a chemical process based on automatic clustering algorithms. Due to data preprocessing importance, feature selection was performed by a non-dominated sorting genetic algorithm (NSGAII) based on k-means clustering. The optimal subset of features is selected by comparing clustering results for each subset. The performance of the proposed feature selection method was compared with the Fisher discriminant ratio (FDR), and XGBoost methods. The t-distributed stochastic neighbor embedding (t-SNE), Isomap, and KPCA dimension reduction methods were also employed for feature extraction. Finally, automatic clustering was performed based on metaheuristic algorithms for fault detection and diagnosis. Results were compared with non-automatic clustering methods. The performance of the proposed method was evaluated by examining the Tennessee Eastman and four water tank processes as case studies. The results showed that the proposed method is reliable and capable of online and offline chemical process fault detection and diagnosis. As a result, the findings of this study can be used to stabilize the operation of chemical processes.

An efficient automatic clustering algorithm for probability density functions and its applications in surface material classification

AbstractClustering is a technique used to partition a dataset into groups of similar elements. In addition to traditional clustering methods, clustering for probability density functions (CDF) has been studied to capture data uncertainty. In CDF, automatic clustering is a clever technique that can determine the number of clusters automatically. However, current automatic clustering algorithms update the new probability density function (pdf) based on the weighted mean of all previous pdfs , resulting in slow convergence. This paper proposes an efficient automatic clustering algorithm for pdfs. In the proposed approach, the update of is based on the weighted mean of , where is the number of pdfs and . This technique allows for the incorporation of recently updated pdfs, leading to faster convergence. This paper also pioneers the applications of certain CDF algorithms in the field of surface image recognition. The numerical examples demonstrate that the proposed method can result in a rapid convergence at some early iterations. It also outperforms other state‐of‐the‐art automatic clustering methods in terms of the Adjusted Rand Index and the Normalized Mutual Information. Additionally, the proposed algorithm proves to be competitive when clustering material images contaminated by noise. These results highlight the applicability of the proposed method in the problem of surface image recognition.

A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.

Автоматическая настройка программных средств размещения пользовательских схем на ПЛИС

Searching for new ways to improve the efficiency of integrated circuits (IC) led to the development of specialized heterogeneous configurable IC (FPGA) and systems-on-a-chip. Their key feature is an extended interpretation of standard cell library, containing ready-to-use IP cores along with standard cells. Specific customer designs require the flexibility of the configurable heterogeneous IC’s architecture and, therefore, automatic CAD clustering and placement algorithms configuration. The development of efficient configuration methods and algorithms is impossible without relying on the mathematical apparatus. In this work, such mathematical apparatus is provided. The authors described a set-theoretic model of a hierarchical project and formalized the hierarchical approach to the netlist, using the apparatus of mathematical logic, set and graph theories. The correspondence between the customers designs’ elements and FPGA’s elements has been formalized to provide fast clustering and placement configuration. The obtained results provide the basis for future efficient methods for automatic placement and clustering configuration.

A New Clustering Approach for Automatic Oscillographic Records Segmentation

The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is so high that it makes human analysis infeasible. An alternative way of solving this problem is to group events in similar classes so that it is no longer necessary to analyze all the events, but only the most representative of each class. Several automatic clustering algorithms have been proposed in the literature. Most of these algorithms use validation indexes to rank the partitioning quality and, consequently, find the optimal number of clusters. However, this issue remains open, as each index has its own performance highly dependent on the data spatial distribution. The main contribution of this paper is the development of a methodology that optimizes the results of any clustering algorithm, regardless of data spatial distribution. The proposal is to evaluate the internal correlation of each cluster to proceed or not in a new partitioning round. In summary, the traditional validation indexes will continue to be used in the cluster’s partition process, but it is the internal correlation measure of each one that will define the stopping splitting criteria. This approach was tested in a real waveforms database using the K-means algorithm with the Silhouette and also the Davies–Bouldin validation indexes. The results were compared with a specific methodology for that database and were shown to be totally consistent.

A collaborative decision support system for multi-criteria automatic clustering

Automatic clustering is a challenging problem, especially when the decision-maker has little or no information about the nature of the dataset and the criteria of interest. There is a lack of generalizability in the current validity indexes (VI) for automatic clustering algorithms, as each considers a limited number of objectives and mostly ignores the other aspects of clustering validation. The proposed framework benefits from collaboration among selected evolutionary algorithms. A mixed-integer non-linear programming model is developed, and a framework is proposed for a six-step decision support system to solve it. The decision-maker (DM) selects the quantitative (primary) VIs and the evolutionary algorithms. Given DM's knowledge on the dataset and VIs, DM can incorporate qualitative (secondary) VIs. DM determines the quality threshold for each VI and runs the evolutionary algorithms separately. The DSS then saves the best obtained value of VIs in order to prepare the input necessary to construct the aggregated function. Based on the selected primary VIs, a new normalized aggregated function is developed and solved repeatedly using the randomly selected or predefined weights of importance. Eventually, DM employs a proper DEA model to define the final clustering output among all possible solutions. Given multiple efficient solutions, the best-worst method and a multi-criteria decision-making approach are applied to find the final output. The applicability of the proposed approach is illustrated on a synthetic and two secondary datasets, and the result at each step is discussed in detail.

Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

Cluster analysis is an essential tool in data mining. Several clustering algorithms have been proposed and implemented, most of which are able to find good quality clustering results. However, the majority of the traditional clustering algorithms, such as the K-means, K-medoids, and Chameleon, still depend on being provided a priori with the number of clusters and may struggle to deal with problems where the number of clusters is unknown. This lack of vital information may impose some additional computational burdens or requirements on the relevant clustering algorithms. In real-world data clustering analysis problems, the number of clusters in data objects cannot easily be preidentified and so determining the optimal amount of clusters for a dataset of high density and dimensionality is quite a difficult task. Therefore, sophisticated automatic clustering techniques are indispensable because of their flexibility and effectiveness. This paper presents a systematic taxonomical overview and bibliometric analysis of the trends and progress in nature-inspired metaheuristic clustering approaches from the early attempts in the 1990s until today’s novel solutions. Finally, key issues with the formulation of metaheuristic algorithms as a clustering problem and major application areas are also covered in this paper.

Automatic clustering by multi-objective genetic algorithm with numeric and categorical features

Many clustering algorithms categorized as K-clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K-clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K-Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GAs) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm.

Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data

Categorical data clustering has been attracted a lot of attention recently due to its necessary in the real-world applications. Many clustering methods have been proposed for categorical data. However, most of the existing algorithms require the predefined number of clusters which is usually unavailable in real-world problems. Only a few works focused on automatic clustering, but mainly handled for numerical data. This study develops a novel automatic fuzzy clustering using non-dominated sorting particle swarm optimization (AFC-NSPSO) algorithm for categorical data. The proposed AFC-NSPSO algorithm can automatically identify the optimal number of clusters and exploit the clustering result with the corresponding selected number of clusters. In addition, a new technique is investigated to identify the maximum number of clusters in a dataset based on the local density. To select a final solution in the first Pareto front, some internal validation indices are used. The performance of the proposed AFC-NSPSO on the real-world datasets collected from the UCI machine learning repository exhibits effectiveness compared with some other existing automatic categorical clustering algorithms. Besides, this study also applies the proposed algorithm to analyze a real-world case study with an unknown number of clusters.

The Cluster of City Buildings Based on the SOM Neural Network

The clustering of buildings on the city map is an important field of cartographic and the main step to resolve issues related to the scale of the city map transformation process. The development of computer technology promotes the studies of cartographic and modelling algorithms. In order to achieve automatic clustering of map elements, the domestic and foreign scholars have proposed numerous automatic clustering algorithms. Although each algorithm works for specific problems, all algorithms are based on the graphical analysis transformation of building polygons on the map. By reading and analyzing current development of domestic and foreign researches, we find out that map building clustering needs more inspiring interactive features and automatic integrated decision-making simulation functions. It needs to improve the visualization software and integrated automatic intelligence, increase the proportion of the batch to support implementation of automated cartographic generalization to a higher level.

Automatic clustering algorithms for indoor site selection in LTE

Small cell systems are a cost-effective solution to provide adequate coverage inside buildings. Nonetheless, the addition of any indoor site requires evaluating the trade-off between the coverage and capacity gain provided by the new site and its monetary cost. In this paper, a new automatic indoor site selection algorithm based on clustering techniques is presented. The algorithm calculates the number of antennas, radio heads, and baseband units needed for the area under study. Then, a clustering algorithm groups several radio heads of different buildings in a single pooled baseband unit, reducing deployment costs. The proposed clustering algorithm is based on a local refinement algorithm, whose starting point considers a new baseband unit for every new site, and then, possible reallocation to existing units is checked. To assess the method, the proposed indoor site selection algorithm is included in a network planning tool. The algorithm is tested in a real heterogeneous network scenario, taking into account vendor specifications and operator constraints. Results show that the use of the proposed clustering algorithm can reduce the total network cost by up to 58 % in a real scenario.

An automatic clustering algorithm inspired by membrane computing

Membrane computing is a class of distributed parallel computing models. Inspired from the structure and inherent mechanism of membrane computing, a membrane clustering algorithm is proposed to deal with automatic clustering problem, in which a tissue-like membrane system with fully connected structure is designed as its computing framework. Moreover, based on its special structure and inherent mechanism, an improved velocity-position model is developed as evolution rules. Under the control of evolution-communication mechanism, the tissue-like membrane system cannot only find the most appropriate number of clusters but else determine a good clustering partitioning for a data set. Six benchmark data sets are used to evaluate the proposed membrane clustering algorithm. Experiment results show that the proposed algorithm is superior or competitive to three state-and-the-art automatic clustering algorithms recently reported in the literature.

Adapt DB-PSO patterns clustering algorithms and its applications in image segmentation

Clustering algorithm is a crucial step before to analysis object's feature in image applications. The adapt DB-PSO patterns clustering algorithms (ADPCA) combined the particle swarm optimization (PSO) clustering algorithm and adapt DB_index measuring methodology to efficiently decide the real number of clusters, cluster centers, and then to recognize the correct catalog even if there are exiting some cases in various shapes, multi-dimension, real life training patterns and image datasets. In general, the PSO is adapted for dealing complex and global optimization problems. The population-based evolutional PSO learning algorithm with the self-adapt mathematic index can fit the data vibration to perform the real criterion of homogeneity of neighboring pixels in many image vision and understanding cases. Owing to the purpose of generating automatic clustering algorithms, the specific fitness function contains the DB_validity measure to significantly improve resolutions of spatial information among the given training patterns. The computation of image DB_index is delivered to retrieve the specific objects by evaluating the characters of given patterns. The novel ADPCA actually indicate the homogeneity region of interesting pictures and eliminate small pieces of elements by the supports of DB index measure, which can be used to dynamically compute the maximal similarity and small difference of the discussed image patterns. Several artificial datasets include the three-dimensional dataset with five spherical clusters, two-dimensional patterns with three different sizes circles, one Chtree Fractal image patterns, one real life IRIS data and one grey level image data, which are given as training patterns to demonstrate the adaptation and efficiency of the ADPCA learning method. It presents that ADPCA determine the correct clustering number and suitable cluster position in different data clustering examples. Two image segmentation applications also show that ADPCA can achieve correct detection of subjects. In conclusion, several simulations compared with the traditional k-means algorithm demonstrate the great results of ADPCA learning machine.

Автоматическая генерация программ для графических процессоров по непроцедурным спецификациям

The mesoscale convective system (MCS) cinematic and evolution routinely derived from realtime radar reflectivity Z data are used to develop 3 step short-range forecasting technique. The first forecasting step is estimation of MCS translation velocity. It is proposed that translation can be estimated by robust interactive procedure, identifying and tracking of “conservative meso-β fragments”, or by automatic clustering algorithms. The second step is nowcasting when entire Z field is extrapolated by translation to forecast time ~0,5-1 hours. The third step is forecast verification and MCS diagnostic when prognostic and real-time images of Z are coupled to one composite image, denominated as life-cycle display (LCD). The construction of LCD is adapted to use both in forecast verification in terms of FAR, POD and CSI and to display 3 type conventionally outlined MCS areas where: 1) ongoing convection occur; 2) dissipating convection/stratiform transformation and 3) precipitation was recently ceased. As poor extrapolation forecasts associated, mainly, with growth and decay of storms in the forecast period, life-cycle composite images provide valuable information about of developing, dissipating areas and areas prohibited to new convective development.

Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery

Traditional automatic fuzzy clustering methods can obtain the optimal number of clusters by maximizing or minimizing one single-objective function using validity indexes. However, the effectiveness of these methods depends on the selection of the validity indexes, and one single-objective function may not provide satisfactory results because of the complexity of remote sensing images. For instance, the same land types may have different spectral curves, and different land types can have similar curves. To avoid this problem, this paper proposes a novel automatic fuzzy clustering method based on adaptive multi-objective differential evolution (AFCMDE) for remote sensing imagery. In AFCMDE, the automatic clustering problem is transformed into a multi-objective problem using two objective functions: Jm and the Xie-Beni index. AFCMDE is designed as a two-layer system comprising an optimization layer and a classification layer. In the optimization layer, AFCMDE searches for a feasible number of clusters by minimizing the Jm value and the Xie-Beni index. Based on the obtained number of clusters, AFCMDE utilizes non-dominated and crowd-distance sorting to obtain the optimal clustering centers and output the clustering results. In addition, a self-adaptive strategy without user-defined parameters is also used to improve the differential evolution. Experimental results using three different types of remote sensing image show that the AFCMDE algorithm consistently outperforms the other traditional clustering algorithms and the previous single-objective automatic fuzzy clustering algorithms.

Using the Kurtosis Measure to Identify Clusters in Wireless Channel Impulse Responses

In wireless channel propagation, multipath arrivals appear at the receiver in clusters. Because the notion of clusters tends to be intuitive rather than well-defined, cluster identification in channel impulse responses has traditionally been carried out through human visual inspection. Besides time-consuming for large-scale measurement campaigns, this approach is subjective and will vary from person to person, leading to arbitrary identification. In response to these concerns, automatic clustering algorithms have emerged in the past decade. Most, however, are laden with settings which are sensitive to different radio-frequency environments, again subject to arbitrariness. In this communication we propose a novel clustering algorithm based on the kurtosis measure which, in related work, has been employed precisely for its channel independence. We compare ours to some recent algorithms through a standard validation procedure on simulated impulse responses. We show the proposed algorithm to deliver better results and, because it requires no channel-specific settings, is inherently robust to different environments. Results of the proposed algorithm are also illustrated on real measurements in four buildings with assorted wall materials.

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.

Automatic classification of protein structures relying on similarities between alignments

BackgroundIdentification of protein structural cores requires isolation of sets of proteins all sharing a same subset of structural motifs. In the context of an ever growing number of available 3D protein structures, standard and automatic clustering algorithms require adaptations so as to allow for efficient identification of such sets of proteins.ResultsWhen considering a pair of 3D structures, they are stated as similar or not according to the local similarities of their matching substructures in a structural alignment. This binary relation can be represented in a graph of similarities where a node represents a 3D protein structure and an edge states that two 3D protein structures are similar. Therefore, classifying proteins into structural families can be viewed as a graph clustering task. Unfortunately, because such a graph encodes only pairwise similarity information, clustering algorithms may include in the same cluster a subset of 3D structures that do not share a common substructure. In order to overcome this drawback we first define a ternary similarity on a triple of 3D structures as a constraint to be satisfied by the graph of similarities. Such a ternary constraint takes into account similarities between pairwise alignments, so as to ensure that the three involved protein structures do have some common substructure. We propose hereunder a modification algorithm that eliminates edges from the original graph of similarities and gives a reduced graph in which no ternary constraints are violated. Our approach is then first to build a graph of similarities, then to reduce the graph according to the modification algorithm, and finally to apply to the reduced graph a standard graph clustering algorithm. Such method was used for classifying ASTRAL-40 non-redundant protein domains, identifying significant pairwise similarities with Yakusa, a program devised for rapid 3D structure alignments.ConclusionsWe show that filtering similarities prior to standard graph based clustering process by applying ternary similarity constraints i) improves the separation of proteins of different classes and consequently ii) improves the classification quality of standard graph based clustering algorithms according to the reference classification SCOP.

Gene transposon based clone selection algorithm for automatic clustering

Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for automatic clustering named as Gene Transposon based Clone Selection Algorithm (GTCSA) is proposed in this paper. The proposed algorithm does not require a prior knowledge of the number of clusters; an improved variant of the clonal selection algorithm is used to determine the satisfied number of clusters and the appropriate partitioning of the data set as well. In addition, a novel operation called antibody gene transposon is introduced to the framework of clonal selection algorithm which can realize to find the satisfied number of cluster automatically. The proposed method has been extensively compared with iterated local search approach (ILS) and three well-known automatic clustering algorithms, including automatic clustering using an improved differential evolution algorithm (ACDE); variable-string-length genetic algorithm based clustering techniques (VGA) and the dynamic clustering approach based on particle swarm optimization (DCPSO). 23 datasets with widely varying characteristics are used to demonstrate the superiority of the GTCSA. In addition, GTCSA is applied to a real world application, namely natural image segmentation, with a good performance obtained.

A two-stage genetic algorithm for automatic clustering

In this paper, a two-stage genetic clustering algorithm (TGCA) is proposed. This algorithm can automatically determine the proper number of clusters and the proper partition from a given data set. The two-stage selection and mutation operations are implemented to exploit the search capability of the algorithm by changing the probabilities of selection and mutation according to the consistence of the number of clusters in the population. First, the TGCA focuses on the search of the best number of clusters, and then gradually transfers towards finding the globally optimal cluster centers. Furthermore, a maximum attribute range partition approach is used in the population initialization so as to overcome the sensitivity of clustering algorithms to initial partitions. Finally, the efficiency of TGCA has been extensively compared with several automatic clustering algorithms, including hierarchical agglomerative k-means, automatic spectral algorithm and a standard genetic k-means clustering algorithm (SGKC). Experimental results on four artificial and seven real-life data sets show that the TGCA has derived better performance on the search of the cluster numbers and higher accuracy on clustering problems.