k-means algorithm

Cloud computing provides computational services such as servers, memory, storage space, databases, networks, software, analytics, and information as virtualized resources through the internet to offer faster innovation, flexible resources, and cost savings at scale. Although cloud computing service providers are innovatively expanding their services, trust is one of the major obstacles to the progress of this matter. Trust is the biggest issue in cloud computing since trust is an effective guarantor during interactions between the users and the providers. Trust is one of the most fundamental methods for increasing confidence in resources provided in the cloud environment and is important in cloud business environments. With the increasing number of cloud services providers in the cloud computing environment and the number of users, the selection of provider has become a major challenge. The Coa algorithm has a higher convergence speed, at least by 5.9%, compared to the studied algorithms. In this research, an optimization approach based on a metaheuristic process using the COA algorithm combined with the K-means clustering algorithm is proposed to solve the optimization problem of selecting the best provider in the trust management third-party component layer based on parameters. In this method, while reducing trust evaluation time, the accuracy of user access to the most trusted provider based on user priorities has increased compared to previous methods. This can increase user confidence and improve the quality of service providers.

Each driving cycle designed to monitor fuel consumption, vehicle emissions, and the specific driving patterns of a region is created to facilitate better planning and enhancement of local standards. In this study, the Real Driving Emissions (RDE) method, regarded as one of the most precise techniques for determining driving cycles, was employed. To gather the necessary data, GPS from mobile phones was utilized while traveling on city buses through the streets of Kermanshah. This data collection included recording speed, acceleration, duration of constant speed, deceleration, altitude, number of braking events, and the duration of stops along the route from origin to destination. The research employed the clustering of real city data and the K-means method to derive the driving cycle for Kermanshah’s city buses. Analyzing the collected data reveals that bus drivers in Kermanshah tend to avoid rapid acceleration and high constant speeds, leading to reduced fuel consumption in the city bus cycle, indicating that this behavior is intentional and aimed at this goal.

Nowadays, clustering plays an important role in most research fields such as engineering, medicine, biology, data mining, etc. In fact, clustering means unsupervised division. By using it, the data are divided into categories that are more similar to each other in terms of the parameters of interest. One of the famous methods in this field is k-means. In this method, despite the dependence on initial conditions and convergence to local optimal points, N numbers of data are grouped into k clusters with high speed. In this article, to solve the existing problems, the combined method is used based on evolutionary algorithms, chaos theory and k-means; that is in addition to solving the mentioned problems, it will also be independent of the number of variables. In this article, for the purpose of validation, the proposed methods are implemented on 13 different famous collections, and the results are compared with genetic algorithm, particle community, bee colony, simulated refrigeration, differential evolution, harmony search, and k-means methods. The high ability and robustness of these methods will be evident based on the results.

Due to the high share of electricity consumption in the country's industries, in recent years, various projects have been implemented, including reducing the amount of load by implementing nationwide blackouts during peak consumption times. Today, data mining is widely used as a process of discovering useful patterns from the database and one of the effective methods for analyzing, modeling and predicting energy consumption. In this study, an integrated clustering-association data mining model has been designed to investigate power consumption behavior to discover and extract the pattern from the power consumption data set of industrial units located in one of the industrial towns of Tehran province. Observations show that during the warm months of the year, the average consumption of high-consumption cluster units, which includes about 34% of the studied industrial units, is about 4.2 times the consumption of low-consumption clusters and about 1.7 times the consumption of medium clusters. By using the proposed model in this study, in addition to identifying high-consumption units and implementing smart and fair policies in forced shutdowns, it is possible to prevent damage caused by forced shutdowns and industrial units can be encouraged to optimize energy consumption. The innovative approach of this model is able to control large volumes of data for planning different areas with the aim of optimizing its energy consumption.

Vehicle traffic congestion control has an essential role in time management and with a case study on clustering-based VANET networks, an algorithm for dependable routing using the re-clustering for vehicular networks with v2v communication has been proposed.The aggregation of vehicles has a direct role in controlling traffic congestion, By observation the index parameters, we obtain the objective function, that merging parameter such as weight and distance, it optimizes the route length compared to previous methods, And by re-clustering, we decreased the number of vehicle collisions using the neighborhood principle. In this case, by considering the Euclidean distance, we can prevent the aggregation of vehicles using the minimum distance to create an optimal route. With the help of the objective function, we will achieve a dependable route to optimize packet delivery and the delay END-TO-END.Ultimately, using the proposed an algorithm, By categorization the inputs and real execution time, we increase the dependable routing compared to the SCRS algorithm, and we also decreased fuel consumption.

What is important in the current situation it is important to pay attention to patterns of the water consumption behavior of subscribers and to identify subscribers and consumers with a higher position in the value pyramid in the field of water consumption management policy. Value pyramid is a tool that identifies valuable customers in terms of consumption, and so, has great application and importance in the field of water consumption management to identify high-consumption and low-consumption customers. Therefore, in the present study, in order to identify the patterns of consumption behavior of Shiraz Water and Wastewater Company subscribers based on their consumption value pyramid and to predict the subscribers with a higher position in the value pyramid, data mining techniques have been used. In the framework of the proposed method, first, the data of water subscribers' consumption including residential, commercial and industrial subscribers, public and administrative, religious and educational sites, military and non-governmental, for two consecutive years, were extracted from the database of Shiraz Water and Wastewater Company. After determining the optimal number of clusters using self-organizing map and Davis Bouldin index, clustering operation is performed using K-Means algorithm. It should be noted that the indicators and criteria for subscriber clustering include the type of use, location, consumption, history of unauthorized branching, number of disconnection notices and time of payment of bills which have been identified using the opinion of experts. Then, while calculating the consumption value of subscribers in each cluster and plotting the subscriber consumption value pyramid, the decision tree algorithm is used to predict and discover the behavioral patterns of subscribers. The results show that Shiraz Water and Wastewater Company subscribers are divided into six clusters in terms of consumption behavior patterns. While drawing the consumption value pyramid, these six clusters are classified into three classes: high consumption subscribers, medium consumption subscribers and low consumption subscribers. After implementing the decision tree, the accuracy of the tree was 78.92 that, according to the results of the decision tree, the subscribers of these three classes have 11 patterns of behavior that predict the type of consumption. Thus, according to the 11 behavioral patterns of the subscribers of Shiraz Water and Wastewater Company, the consumption of new subscribers can be predicted and its position in the value pyramid can be determined.

Clustering techniques are used to extract the structure of software for understanding, maintaining, and refactoring. In the literature, most of the proposed approaches for software clustering are divided into hierarchical algorithms and search-based techniques. In the former, clustering is a process of merging (splitting) similar (non-similar) clusters. These techniques suffered from the drawbacks such as finiteness criterion and arbitrary decisions occurred in the process. Because of the NP-hardness of clustering software systems, evolutionary and search-based algorithms are more commonly used algorithm than hierarchical ones. In evolutionary algorithms, the clustering of software systems is considered as a problem of searching over some possible clustering candidates. Although these algorithms are often able to achieve an appropriate structure of the software, they are not applicable in clustering large-scale software. Furthermore, these algorithms are unable to consider the knowledge in the artifact dependency graph, which extracted from the source code of the software. In software systems, an artifact can be everything like a class, a function, or a file. In this paper, a new partition-based clustering algorithm is presented. This algorithm attempts to partition the artifact dependency graph considering the knowledge therein. Moreover, a new distance criterion is presented to measure the similarity and dissimilarity of the artifacts. The proposed algorithm starts with the artifact dependency graph and creates the similarity matrices of the artifacts. So, it attempts to refine the partition candidate until a fixed point is reached. We expect that the proposed method compared with other methods could lead to achieve the clustering with high quality and similar to the expert's clustering based on MoJo-FM measure. To demonstrate the applicability and validity of the proposed algorithm, a large-scale case study, Mozilla Firefox, is employed. The results demonstrate that the proposed algorithm outperforms the commonly used evolutionary methods in the literature.

Driving cycles represent the vehicle speed as a function of time and are used in vehicle design, fuel management, and the improvement of standard indicators. In this study, four combined driving cycles were extracted using real data. The data was collected from a passenger car with a gasoline engine under real driving conditions while driven from Tehran to Amol based on the car chasing method. A code was generated in MATLAB software to create the desired cycle using support vector machine and K-means algorithms considering mid-range and mean values as group centers. The characteristic parameters of the cycles such as the average speed and the percentage of the car travel time at idle, cruise, accelerating, and decelerating conditions were also calculated. These cycles were compared based on the mean relative error, the root-mean-square error, and the Chi-square test. The results showed that the cycles extracted by the support vector machine were closer to the allowable time interval (less than 1800 seconds); however, the cycle extracted by the K-means algorithm with the mean value as the centers of the generated categories, recorded the least errors. This cycle, in addition to spending most of its time in accelerated motion, represented a greater amplitude of acceleration and velocity fluctuations than other cycles.

Due to the increase in wind power production and the increasing development of wind farms its impact on power systems cannot be neglected and modeling of wind farms has been converted to an important topic in power engineering. When studying the effects of a wind farm on the power system, Instead of modeling the wind farm in detail, the performance of the wind farm at the point of the common coupling can be considered as an equivalent model includes an equivalent generator and an equivalent turbine. This approach in addition to simplify the system analysis would considerably reduce the simulations burden. In order to improve the accuracy of this equivalent model the mechanical parts of the wind turbines can be replaced with more than one equivalent turbine using clustering algorithms. Traditionally these algorithms consider the wind speed as the feature for clustering. In this paper it is proposed to classify the wind turbines by power coefficient of each turbine instead of the wind speed. The simulation results obtained using this approach is verified against the conventional approach and also the detailed model. Simulations are done in MATLAB. The results indicate that the accuracy of the equivalent model obtained using proposed approach, is considerably improved.

Blind recognition of error correction codes parameters from intercepted bit-stream at the receiver side, is highly considered in military and commercial applications. In fact, identification of the encoding scheme used in the transmitter without any prior information, is a challenging task to the adversary. Several methods have been presented for blind code recognition. In this paper, a statistical method for recognition of  the length of the code word and the length of the block of information is presented. This scheme not only is resistant to error, but also its performance sustains in long codes. In this work, the method has been tested using some clustering algorithms such as K-Means and Jenks Natural Breaks. Then, a novel method to extract features of systematic binary linear block codes has been presented. Simulation results in MATLAB show that the proposed method, in addition to having low computational complexity and high performance rate, have an acceptable result in identifying systematic block codes with long lengths and even at high error levels.

Extensive available hydrological characteristics (precipitation, temperature, streamflow data and physiographic attributes of catchments) can be used to extract hydrological similar catchments. In this research cluster analysis as a new and effective method, was used for grouping catchments into several groups or clusters. In order to understanding the hydrologic similarity, 28 characteristics (descriptors) of location, physiographic, climatic and land use of 15 catchments with heterogeneous characteristics located in the western part of the Hamoun-Jazmourian river basin were used. Selecting of characteristics were done based on the hydrological response specification which provided insight into the hydrologic performance of the catchments. In RStudio software, using PCA algorithm, the components and main characteristics were extracted, then the number of optimum clusters with the Davies-Bouldin criterion was determined and the clustering of the catchments into homogenous classes was performed using k-means algorithm. The results showed that the latitude of gravity center, area, length of main river, height of hydrometric gauge, slope and percentage of poor rangelands are as the main attributes from 28 attributes, also, the Davies-Bouldin criterion was 2.46 for the number of clusters equal to 3, which indicates the number of clusters in the k-means algorithm. After clustering the catchments, it was determined that most of the catchments in the same clusters are located in the vicinity of each other. The results of this study enable us to interpret the hydrologic behavior in the study area for purposes such as streamflow regionalization in ungauged catchments of this region and regional flood frequency analysis.