Ant Colony Clustering Algorithm Research Articles

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

A self-decision ant colony clustering algorithm for electricity theft detection

The load data features of some electricity-theft consumers during the theft period are similar to those of normal consumers, making these electricity-theft consumers outliers from the cluster of electricity-theft. The current classification method, which uses the mean value to determine the cluster centers, is vulnerable to the influence of outliers. Therefore, this paper proposes a self-decision ant colony clustering algorithm for electricity theft detection method that is targeted to self-decision which samples are used to update the cluster centers. The method constructs a dynamic weighting approach to determine the cluster centers based on the idea of Backpropagation, and updates the weights of each sample in the clusters to reflect the different importance of different samples, thus reducing the influence of outlier samples. A new activation function, Odd, is proposed to enhance the ability of the proposed method to solve linearly indistinguishable problems. A self-decision dropout mechanism is proposed which evolves the mechanism of randomly stopping the work of samples in clusters into a targeted and self-decision mechanism that stops the work of redundant or non-active samples as well as improves the contribution of outlier samples with positive effects. In this paper, the proposed method is tested by the electricity consumption data provided by the State Grid Corporation of China (SGCC) and the Smart* Data Set for Sustainability (SDSS) provided by the UMass Trace Repository, and the experimental results show that the proposed method effectively solves the above problems with higher detection accuracy, it has certain advantages over other current studies.

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Human-caused accident evaluation of coal mine safety relying on ant colony clustering algorithm

Exploration and Practice of Labour Education in Higher Vocational Colleges and Universities in the New Era Based on Big Data Analysis

Abstract To enhance the labor literacy of college students, this paper examines big data in labor education. Firstly, it visualizes and preprocesses labor education big data and designs a method to analyze labor education big data based on an improved ant colony clustering algorithm. Secondly, it examines the labor education of higher vocational colleges in the new era and establishes an evaluation index system for higher vocational labor education in the new era. Finally, labor education is examined using the improved ant colony clustering algorithm, and the practical effects of labor education are examined. Practice shows that ant colony clustering clusters labor education in higher vocational colleges into three categories, in which the proportion of category 1 is 0.28, the proportion of category 2 is 0.41, the proportion of category 3 is 0.31, and the effects of labor concepts, labor knowledge, labor skills, labor qualities, and labor literacy have been improved by 0.27, 0.29, 0.29, 0.26, and 0.24, respectively. Higher vocational colleges and universities of the new era of labor education can improve the labor literacy of working college students.

A self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism

Background and objectiveAs a representative type of cardiovascular disease, persistent arrhythmias can often become life-threatening. In recent years, machine learning-based ECG arrhythmia classification aided methods have been effective in assisting physicians with their diagnosis, but these methods have problems such as complex model structures, poor feature perception ability, and low classification accuracy. MethodsIn this paper, a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism is proposed. This method does not distinguish between subjects when establishing the dataset in order to reduce the effect of differences in ECG signal features between individuals, thus improving the robustness of the model. When the classification is achieved, a correction mechanism is introduced to correct outliers caused by the accumulation of errors in the classification process in order to improve the classification accuracy of the model. According to the principle that the flow rate of gas can be increased under the convergence channel, a dynamically updated pheromone volatilization coefficient ρ, namely the increased flow rate ρ, is introduced to help the model converge more stably and faster. As the ants move, the next transfer target is selected by a truly self-adjusting transfer method, and the transfer probability is dynamically adjusted according to the pheromone concentration and the path distance. ResultsBased on the MIT-BIH arrhythmia dataset, the new algorithm achieved classification of five heart rhythm types, with an overall accuracy of 99.00%. Compared to other experimental models, the classification accuracy of the proposed method represents a 0.2% to 16.6% improvement, and compared to other current studies, the classification accuracy of the proposed method is 0.65% to 7.5% better. ConclusionsThis paper addresses the shortcomings of ECG arrhythmia classification methods based on feature engineering, traditional machine learning and deep learning, and presents a self-adjusting ant colony clustering algorithm for ECG arrhythmia classification based on a correction mechanism. Experiments demonstrate the superiority of the proposed method compared to basic models as well as those with improved partial structures. Furthermore, the proposed method achieves very high classification accuracy with a simple structure and fewer iterations than other current methods.

An empirical study of improved ant colony clustering algorithm in English composition review

An empirical study of improved ant colony clustering algorithm in English composition review

An Empirical Study of Improved Ant Colony Clustering Algorithm in English Composition Review

An Empirical Study of Improved Ant Colony Clustering Algorithm in English Composition Review

An Over Sampling Method of Unbalanced Data Based on Ant Colony Clustering

Aiming at the low classification accuracy of unbalanced data sets, an improved SMOTE over-sampling algorithm ACC-SMOTE (Ant Colony Clustering Synthetic Minority Oversampling Technology) based on ant colony clustering is proposed. On the one hand, the improved ant colony clustering algorithm is used to divide a small number of samples into different sub-clusters, fully considered the imbalance between inter-cluster and intra-cluster data, and SMOTE algorithm is used to oversample the samples according to the proportion of sub-clusters, to reduce the imbalance of intra-class data. On the other hand, Tomek Links data cleaning technology is used to correct the oversampled samples in time, the quality of synthetic samples is guaranteed by eliminating noise in data sets and overlapping samples generated by sampling methods. The training data set and the test data set used in this paper are both UCI data sets. The experimental results show that this algorithm can significantly improve the classification accuracy of a few classes, thus improving the classification performance of the classifier.

Data Mining Model Based on Improved Ant Colony Algorithm

Data mining technology is an important means and way of data analysis in the Internet era, and it is a process of extracting potentially useful information from large databases. From the perspective of theory and technology, data mining is a process of discovering the relationship between data and models or between data from a huge database. From the application level, data mining can provide the government and enterprises with valuable and different levels of knowledge, and provide strong technical support for social development. The existing data methods mainly include classification method, association analysis method, cluster analysis method and anomaly detection method, but these methods have some shortcomings. Aiming at the problems of big randomness, sensitivity to parameters and slow convergence speed of existing ant colony clustering algorithm, this paper proposes a data mining model based on improved ant colony algorithm, which will be applied to cluster analysis of experimental data sets. The experimental results show that the improved algorithm has higher accuracy and faster convergence speed, and can effectively realize data information mining.

Application of ant colony clustering algorithm in coal mine gas accident analysis under the background of big data research

Application of ant colony clustering algorithm in coal mine gas accident analysis under the background of big data research

Research on Optimum Algorithm of Charging Pile Location for New Energy Electric Vehicle

Artificial intelligence algorithms such as ant colony algorithm and neural network do not need to rely on a large amount of gradient information when solving, especially for large-scale complex optimization problems which are difficult to solve by traditional methods, which provides a new perspective and thinking direction for solving such problems. Based on the analysis of the principles and advantages and disadvantages of RBF neural network and ant colony algorithm, this paper proposes a RBF neural network based on genetic mutation improved ant colony clustering algorithm to evaluate the location of charging station. The improved ant colony clustering algorithm based on genetic variation is used to determine the number of hidden layers of RBF neural network, so as to solve the problem that the initial parameters of RBF neural network can not be accurately selected without scientific methods. An example is used to prove the scientific and effectiveness of this method. Finally, the optimal scheme of charging station location is determined by comparing the comprehensive ranking values obtained by various methods used in this paper to judge the advantages and disadvantages of charging station location.

Parallel Semi-Supervised Big Data Clustering Based on Mapreduce Technology

In the area of information technology, a speedy sensational technology is big data. Big data brings tremendous challenges to extract valuable hidden knowledge. Data mining techniques can be used over big data to extract valuable knowledge for decision making. Big data results in high heterogeneity because it consists of various inter-related kinds of objects such as audios, texts, and images. In addition to this, the inter-related kinds of objects carry different information. So, in this paper clustering techniques are introduced to separate objects into several clusters. It also reduces the computational complexity of classifiers. A Possibilistic c-Means (PCM) algorithm was introduced to group the objects in big data. PCM replicated the characteristic of each object to different clusters effectively and it had capability to avoid the corruption of noise in the clustering process. However, PCM is not more efficient for big data and it cannot confine the complex correlation over multiple modalities of the heterogeneous data objects. So, a Parallel Semi-supervised Multi-Ant Colonies Clustering (PSMACC) is introduced for big data clustering. Initially, the PSMACC splits the data into number of partitions and each partition is processed in mappers. Each mapper generates a diverse collection of three clustering components using the semi-supervised ant colony clustering algorithm with various moving speeds. Then, a hyper graph model was used to combine three clustering components. Finally, two constraints such as Must-Link (ML) and Cannot-Link (CL) are included to form a consensus clustering. Finally, the intermediate results of each mapper are combined in the reducer. However, the overhead of iteration in PSMACC is overwhelming which affects the performance of PSMACC. So, a Parallel Semi-supervised Multi-Imperialist Competitive Algorithm (PSMICA) is proposed to cluster the big data. In PSMICA, each mapper processes the ICA where initial population is called countries. Some of the best countries in the population chosen as the imperialists and the remaining countries form the colonies of these imperialists. The colonies move towards the imperialists based on the distance between them. The intermediate results of each mapper are combined in reducer to get the final clustering result.

Self-organized design of virtual reality simulator for identification and optimization of healthcare software components

As in the current trend, the virtual reality-based application is very popular in the medical healthcare system to generate a realistic virtual 3-D simulation environment that users can interact with specialized devices. The increasing demand for advancement in the requirement of the virtual environment of healthcare policies as well as the systems needs changes in the simulation environment. In reference to such requirement, the software industry needs improvement in the development process which reduces the effect of software cost, complexity and resource planning. From last few years, optimization in development of simulation environment’s cost-benefit aspects is also the challenging area. In view of such issues, there are several guided (supervised) and unguided (unsupervised) algorithms are using evolutionary approaches and nature-inspired self-organized swarm intelligence approaches have been developed by the researchers for virtual real-time healthcare search based software system. However, there is still a gap in the development of such a simulation environment for identification of the software component. This paper proposes a self-organizing component identification technique using medoid based ant colony clustering algorithms. The proposed algorithm has been compared to classical centroid-based clustering (K-means,CRUD, and FCA) and evolutionary approach(genetic algorithm) on the case study of the virtual real-time healthcare system. For the accuracy and precision of the approach two already studied cases have also been used.

The Ant Colony Optimization of Clustering Problems in Data Mining

Clustering analysis is widely used in engineering, it is an important part of data mining. Ant colony algorithm has robust and adaptable characters as new optimization algorithm. This research describes ant colony clustering algorithms characteristics, according to the existing ant colony algorithms. Although ant colony algorithm has obtained many improvements, there are many researches and applications in data clustering, but there are still some deficiencies that need further study.

Affect-Adaptive Activities in a Personalised Ubiquitous Learning System

Many literatures have shown the importance of emotion in learning because of their effect on learner’s performances thereby giving reasons why learners’ affective states are crucial to learning. In this light, this research work aims to identify affect-adaptive activities that would improve learning in a personalised ubiquitous learning system by detecting learning style and affective states through some patterns of  behaviours. In the study, learners’ preference are determined, learners affective states such as confidence, effort, independence and confusion are investigated. The ant colony clustering algorithm is used to determine learners’ activities. The four affective states are determined from the learners’ forum threads, content activities, assessment, timing, and discussion through learners’ engagement. The result of this research on two separate courses, Course A (Affect) and Course B (Non-Affect) shows that the four affective states influence learning. The average mean and standard deviation (SD) value for Course A (Mean=276.23, SD=272.27) over Course B (Mean=200.58, SD=210.51) showed an improvement in learning performance and the t-Test carried out between the courses suggested that students’ performance is dependent on their affective states. https://doi.org/10.26803/ijlter.17.7.3

Optimization Research of Decision Support System Based on Data Mining Algorithm

In order to analyze and make use of data and information more accurately and efficiently, the clustering algorithm is further studied to a certain extent, especially in the process of clustering, further analyzes and refines the processing data. In the field of application of previous clustering of mathematical statistics such as pattern recognition, do some reference, especially in the ant colony clustering algorithm in data aggregation is proposed based on the principle of solving business decision support system to deal with data in the huge data processing result is not ideal problem. The data processing steps of data mining are studied. Through information entropy and ant colony clustering algorithm to achieve this process, the original data for reasoning and verification is also used and the effect before and after the improvement are compared. At the same time, this research provides effective decision support for website construction in contemporary e-commerce field. Taking e-commerce website browsing path as an example, applying the idea of ant colony clustering algorithm based on information entropy to carry out path analysis, five kinds of path types are got. This study can be used as a reference for the construction of other e-commerce websites.

A clustering ensemble learning method based on the ant colony clustering algorithm

Ensemble-based learning is a successful approach for robust partitioning. Since the ensemble classifiers cover each other fault, classification is a critical task. Clustering ensemble based learning can also be done using fusion of some primary partitions which derive from naturally different sources. In this study, a novel clustering ensemble learning method inspired from the ant colony clustering algorithm is proposed. Since ensemble methods necessarily rely on diversity, swarm intelligence algorithms, such as ant colony, are can be good options to be applied. Executing this algorithm for several times on a dataset, result in various partitions. Then, a simple partitioning algorithm is exercised to aggregate them into a consensus partitioning. The proposed clustering approach lets the parameters be free to be manipulated, and thanks to the ensemble, non-optimality of the parameters is covered. Experimental results on several real datasets illustrate the efficiency of the proposed method to generate the final partitioning.

A clustering ensemble learning method based on the ant colony clustering algorithm

Ensemble-based learning is a successful approach for robust partitioning. Since the ensemble classifiers cover each other fault, classification is a critical task. Clustering ensemble based learning can also be done using fusion of some primary partitions which derive from naturally different sources. In this study, a novel clustering ensemble learning method inspired from the ant colony clustering algorithm is proposed. Since ensemble methods necessarily rely on diversity, swarm intelligence algorithms, such as ant colony, are can be good options to be applied. Executing this algorithm for several times on a dataset, result in various partitions. Then, a simple partitioning algorithm is exercised to aggregate them into a consensus partitioning. The proposed clustering approach lets the parameters be free to be manipulated, and thanks to the ensemble, non-optimality of the parameters is covered. Experimental results on several real datasets illustrate the efficiency of the proposed method to generate the final partitioning.

Research on Application of Ant Colony Clustering Algorithm to Xinjiang Ethnic Groups’ Evaluation System of Chinese Teaching

With the continuous development of science and technology, computer technology and clustering technology of DM have become a part of the process of data analysis in this new era. How to use data mining technology to have better service for teaching evaluation field has been a research focus in the related research institutions at home and abroad. This paper has a research on application of ant colony clustering algorithm to Xinjiang ethnic groups’ evaluation system of Chinese teaching based on above. After briefly introducing the related theory and technology, this paper uses exploratory research method and survey method and designs evaluation system of Chinese teaching based on ant colony algorithm. Through the intensive study of a variety of data mining algorithm, this paper designs evaluation model of Chinese teaching using AHP. Then this paper gets data index through related survey and measures results after introducing ant colony clustering algorithm in evaluation system of Chinese teaching. The results show that application of ant colony clustering algorithm to Xinjiang ethnic groups’ evaluation system of Chinese teaching is more effective for teaching evaluation and improves the stability of the evaluation system and makes the system professional and provides effective reference experience to introduce ant colony clustering algorithm to other areas.