An Improved Clustering Method Based on Data Field

Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.

The traditional K-means algorithm is sensitive to the initial clustering center, and randomly selecting different initial clustering centers will result in different clustering results. In this paper, an improved K-means algorithm based on Mean Shift clustering is proposed to solve the existing problems of the K-means algorithm. This algorithm selects a high-density migration vector set MP by Mean Shift, and selects k points with the farthest distance from each other in the high-density region in MP as the initial cluster center. This paper adopts the iris data set and the wine data set from the international standard UCI database, and 150 vowel image texts on the upper part of the baseline for the text analysis of the Ujin body Tibetan ancient books are used to verify the proposed algorithm (The real sample is called the Tibetan dataset). It can be seen from the experimental results that the algorithm can achieve better clustering results with higher accuracy and more stability.

Purpose – It is well known that the selection of initial cluster centers can significantly affect K-means clustering results. The purpose of this paper is to propose an improved, efficient methodology to handle such a challenge. Design/methodology/approach – According to the fact that the inner-class distance among samples within the same cluster is supposed to be smaller than the inter-class distance among clusters, the algorithm will dynamically adjust initial cluster centers that are randomly selected. Consequently, such adjusted initial cluster centers will be highly representative in the sense that they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can then be effectively reduced. In addition, the algorithm is able to obtain all initial cluster centers simultaneously (instead of one center at a time) during the dynamic adjustment. Findings – Experimental results demonstrate that the proposed algorithm greatly improves the accuracy of traditional K-means clustering results and, in a more efficient manner. Originality/value – The authors presented in this paper an efficient algorithm, which is able to dynamically adjust initial cluster centers that are randomly selected. The adjusted centers are highly representative, i.e. they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can be effectively reduced so that the authors can achieve an improved clustering accuracy. In addition, the algorithm is a cost-efficient one and the enhanced clustering accuracy can be obtained in a more efficient manner compared with traditional K-means algorithm.

The traditional K-means clustering algorithm has the problems that the number of clusters needs to be determined artificially, the clustering results are easily affected by the initial clustering centers and isolated points, and the iterative process is computationally complicated. To address above problems, an improved K-means clustering algorithm combining multi-point optimization(MFK-means) is proposed. The proposed algorithm is improved from the following four aspects. Firstly, the number of clusters is jointly determined by combining the contour coefficient method and the elbow rule. Secondly, the optimized outlier detection algorithm is used to exclude the influence of isolated points on the clustering results. Thirdly, the initial clustering centers are gradually determined based on the outlier candidate set and the maximum-minimum distance idea. Finally, the heuristic method is used to reduce the computational effort of the iterative process. The experimental results on the UCI dataset show that the proposed improved algorithm has higher clustering accuracy and better stability than the traditional K-means algorithm and another improved algorithm.

For such difficulties as low mining efficiency and large amount of data that the data mining of residential electricity data has to be faced with, the research on improved K-means algorithm and cloud computing-based analysis on massive data of power utilization is performed. Firstly, as the initial cluster center and the value K is difficult to determine in traditional K-means algorithm, an improved K-means algorithm based on density is proposed. Defining the product of sample density, the reciprocal of the average distance between the samples in the cluster, and the distance between the clusters as weight product, the initial center is determined successively according to the maximum weight product method and the accuracy of the clustering is improved. Then, the parallelization of improved K-means algorithm based on MapReduce model is realized and the efficiency of clustering is improved. Finally, the mining experiment of massive power utilization data is carried out on basis of 400 households' electricity data. Taking family as unit, calculate such features as electricity consumption rate during peak hour, load rate, valley load coefficient and the percentage of power using during normal hour, and establish the data vector dimension, completing the clustering of similar user types, while analyzing the behavioral characteristics of each types of users. Experiment results on Hadoop cluster show that the improved algorithm can be stable and efficient operated and it can achieve better clustering effect.

In China, Hangzhou is the first city to set up the Public Bicycle System. Now, the System has been the largest bike- sharing program in the world. The software of Hangzhou Public Bicycle System was developed by our team. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, determining how to find the key stations to give special care is very important. In this paper, a improved k-means algorithm is used to recognize the key stations of Hangzhou Public Bicycle System. At first, by passing over the two week’s real data, a rent-return database is initialed. Then the algorithm builds minimum spanning tree and splits it to gets k initial cluster centers. The key stations are determined from the rent-return database by the algorithm. Practice examples and comparison with the traditional k-means algorithm are made. The results show that the proposed algorithm is efficient and robust. The research result has been applied in Hangzhou.

For the problem of low accuracy by the traditional K-means clustering algorithm to segment noised brain magnetic resonance imaging (MRI) images. This paper proposed an improved K-means algorithm. The traditional K-means algorithm only considers the brain image gray value itself, ignoring the relationship between pixels. Due to the characteristics of brain MRI image adjacent pixels most likely belonging to the same class, this paper adopts average value of small neighborhood of each image pixel and image gray value to compose a new sample point, in order to reduce the impact of noise on the clustering accuracy. Experimental results show that the improved K-means algorithm can effectively improve the segmentation accuracy of the noised brain MRI image.

Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means algorithm is simple and efficient, it is applied to data mining, knowledge discovery and other fields. However, the algorithm has its inherent shortcomings, such as the K value in the K-means algorithm needs to be given in advance; clustering results are highly dependent on the selection of initial clustering centers and so on. In order to adapt to the historical data clustering of the geological disaster monitoring system, this paper presents a method to optimize the initial clustering center and the method of isolating points. The experimental results show that the improved k-means algorithm is better than the traditional clustering in terms of accuracy and stability, and the experimental results are closer to the actual data distribution.

Aiming at the shortcomings of the K-Means algorithm in the traditional K-Means algorithm, the DBSCAN algorithm is used to divide the order set according to the density, and obtain the batch number K value and the initial cluster center point. Based on this, the improved K-Means algorithm is used for optimization. Based on the real environment and instance data, the established batch assignment batch model is simulated. The experimental results show that the density-based K-Means clustering algorithm can effectively shorten the picking time and improve the warehouse logistics operation.

As online courses are more and more widely used in college courses, the analysis of online learning data has become an important means to improve teaching quality and learning effect. In this paper, an online learner clustering algorithm that integrates Autoencoder and Canopy-Kmeans is proposed. The Autoencoder is used to perform feature degradation and feature extraction for high-dimensional data which effectively solves the problems of data sparsity and dimensionality disaster. The Canopy algorithm is introduced to precluster the data which reduces the randomness of the K-means algorithm during initialization and improves the stability of clustering. And the improved K-means algorithm is used to perform the clustering analysis of text data. The experimental results show that the algorithm proposed behaved better in both clustering accuracy and noise resistance compared with traditional clustering algorithm, which is helpful to a better understanding of the behavioral characteristics of online learners and a better support for personalized teaching is provided based on this.

Clustering is one of the relevant data mining tasks, which aims to process data sets in an effective way. This paper introduces a new clustering heuristic combining the E-transitive heuristic adapted to quantitative data and the k-means algorithm with the goal of ensuring the optimal number of clusters and the suitable initial cluster centres for k-means. The suggested heuris-tic, called PFK-means, is a parameter-free clustering algorithm since it does not require the prior initialization of the number of clusters. Thus, it generates progressively the initial cluster centres until the appropriate number of clusters is automatically detected. Moreover, this paper exposes a thorough comparison between the PFK-means heuristic, its diverse variants, the E-Transitive heuristic for clustering quantitative data and the traditional k-means in terms of the sum of squared errors and accuracy using different data sets. The experiments results reveal that, in general, the proposed heuristic and its variants provide the appropriate number of clusters for different real-world data sets and give good clusters quality related to the traditional k-means. Furthermore, the experiments conducted on synthetic data sets report the performance of this heuristic in terms of processing time.

In order to solve the challenge that are caused by the traditional k-means algorithm such as local optimal solution, initial value selection with randomness, sensitivity to outlier. An improved K-means clustering algorithm based on density radius was proposed. First, remove the outliers in the dataset according to the Local outlier factor (lof). Then calculate the sample point density within the sample point density radius, and the initial cluster center and k value are selected and determined according to the density. After that according to the traditional K-means thoughts to cluster and get new clusters center. Finally, a k-value optimization strategy based on Bayesian Information Criterion (BIC) score is proposed to optimize the k-value and effectively improve the clustering quality. The theoretical analysis and simulation results show that the improved algorithm improves accuracy.

In the past two or three years, genome sequencing technology has been rapidly developed. Large-scale sequencing projects such as the Human Genome Project and the Cancer Genome Project have been launched one after another. Up to now, due to the emergence and research of artificial intelligence, it has brought us many possibilities. The purpose of this article is to use artificial intelligence to help single-cell transcription sequencing as much as possible. Based on the idea of Euclid algorithm, an improved K-means algorithm is proposed, which to a certain extent avoids the phenomenon of clustering results falling into local solutions, and reduces the appearance of the original K-means algorithm due to the use of error squares criterion function. In the case of dividing large clusters, the simulation experiment results show that the improved K-means algorithm is better than the original algorithm and has better stability.

Radar signal sorting is one of the essential technologies in radar countermeasures reconnaissance system. Non-cooperative radar signal sorting without prior information has been a great challenge for radar countermeasures. This paper presents a k-means clustering radar signal sorting algorithm based on data field by introducing data field theory. Firstly potential value of all the data samples is calculated with the algorithm based on data field theory, and noise is eliminated by comparison of the calculated values, to find local maximum potential value. Then the sample data of the closest value to maximum is selected as the initial cluster center and the number of local maximum potential value as cluster number. Finally, the conventional k-means clustering algorithm is applied for clustering. The proposed method can eliminate noise points and automatically obtain initial cluster center and cluster number, which is suitable for non-cooperative radar without prior information. Simulation results verify the feasibility and effectiveness of the method.

K-means algorithms are a group of popular unsupervised algorithms widely used for cluster analysis. However, the results of traditional K-means clustering algorithms are greatly affected by the initial clustering center, with unstable accuracy and low speed, which makes the algorithm hard to meet the requirements for Big Data. In this paper, a modernized version of the K-means algorithm based on density to select the initial seed of clustering is proposed. Firstly, Kd-tree is used to divide the hyper-rectangle space, so those points close to each other are grouped into the same sub-tree during data pre-processing, and the generalized information is stored in the tree structure. Besides, an improved Kd-tree nearest neighbor search is used in the K-means algorithm to prune the search space and optimize the operation for speedup. The clustering results show that the clusters are stable and accurate when the numbers of clusters and iterations are constant. Experimental results in the network intrusion detection case show that the improved version of the K-means algorithms performs better in terms of detection rate and false rate.