A Proposed Modification of K-Means Algorithm

In the era of big data, text as an information reserve database is very important, in all walks of life. From humanities research to government decision-making, from precision medicine to quantitative finance, from customer management to marketing, massive text, as one of the most important information carriers, plays an important role everywhere. The text data generated in these practical problems of humanities research, financial industry, marketing, and other fields often has obvious domain characteristics, often containing the professional vocabulary and unique language patterns in these fields and often accompanied by a variety of “noise.” Dealing with such texts is a great challenge for the current technical conditions, especially for Chinese texts. A clustering algorithm provides a better solution for text big data information processing. Clustering algorithm is the main body of cluster analysis, K-means algorithm with its implementation principle is simple, low time complexity is widely used in the field of cluster analysis, but its K value needs to be preset, initial clustering center random selection into local optimal solution, other clustering algorithm, such as mean drift clustering, K-means clustering in mining text big data. In view of the problems of the above algorithm, this paper first extracts and analyzes the text big data and then does experiments with the clustering algorithm. Experimental conclusion: by analyzing large-scale text data limited to large-scale and simple data set, the traditional K-means algorithm has low efficiency and reduced accuracy, and the K-means algorithm is susceptible to the influence of initial center and abnormal data. According to the above problems, the K-means cluster analysis algorithm for data sets with large data volumes is analyzed and improved to improve its execution efficiency and accuracy on data sets with large data volume set. Mean shift clustering can be regarded as making many random centers move towards the direction of maximum density gradually, that is, moving their mean centroid continuously according to the probability density of data and finally obtaining multiple maximum density centers. It can also be said that mean shift clustering is a kernel density estimation algorithm.

Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm.

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 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.

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 main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centres is used to initialise arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.

For large-scale data sets, the traditional AP clustering algorithm processes all data samples through the idea of iteration and three metrics calculation. This kind of processing will lead to poor clustering effect and high time consumption. Based on this problem, an algorithm which is called KCAP based on Affinity Propagation clustering and Canopy clustering algorithm was proposed. Firstly, KNN distance was used to calculate the local density for each data. Secondly, we used the Canopy algorithm to roughly cluster the data sets, and the center points were formed into a new data set \(Y\). Then, we applied affinity propagation clustering algorithm to cluster the data set \(Y\). Finally, we assigned the data to the nearest center. Compared with the traditional AP algorithm, K-means algorithm and FCM algorithm, the experimental results on synthetic data sets and UCI data sets show that algorithm KCAP not only improves the clustering accuracy, but also have higher efficiency.KeywordsK-Nearest Neighbors (KNN)Initial centerCanopy algorithmAffinity Propagation (AP)Rough clustering

In order to study the K - means algorithm for evaluation of soil fertility, solve the large amount of calculation and high time complexity of the algorithm,this paper proposes the K-means algorithm based on Hadoop platform. First, K-means algorithm is used to cluster for Nongan town soil nutrient data for nine consecutive years; clustering results show that: the accuracy rate increased year by year, and consistent with the actual situation. Then for the K-means clustering algorithm in processing large amounts of data has the disadvantages of high time complexity, This paper uses the K-means algorithm Based on Hadoop platform to realize the clustering analysis of soil fertility of large amounts of data; the results show that: compared with the traditional serial K-means algorithms, improves the operation speed. The above analysis shows that, K- means algorithm is an effective soil fertility evaluation method; Based on Hadoop platform of parallel K-means algorithm has great realistic meaning to analysis of large amount of data of soil fertility factors.

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.

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 process of data mining, the two major problems confronted by K-means clustering analysis are the determination of the initial cluster center and the valuing of k. The traditional K-means algorithm has obvious subjectivity in the above-mentioned two aspects, which will directly affect the clustering effect. In this paper, an analysis method combining relational matrix and degree centrality is proposed to determine the initial center point and the k value of K-means algorithm. The improved K-means algorithm is applied to the clustering analysis of the Chinese entrepreneurial policy text collection, and the clustered topic effects are visually displayed through the word cloud graphs. This empirical analysis not only verifies its effectiveness and objectivity for the improved algorithm in processing large clusters of long text document clusters with random unknown number of categories and category topics, but also provides an approach for the objective classification of Chinese entrepreneurial policy text collections in the meanwhile.

Clustering is a process of classifying data into different classes and has become an important tool in data mining. Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simplicity and high efficiency. However, the traditional K-means algorithm can only find spherical clusters, and is also susceptible to noise points and isolated points, which makes the clustering results affected. To solve these problems, this paper proposes an improved K-means algorithm based on kurtosis test. The improved algorithm can improve the adaptability of clustering algorithm to complex shape datasets while reducing the impact of outlier data on clustering results, so that the algorithm results can be more accurate. The method used in our study is known as kurtosis test and Monte Carlo method. We validate our theoretical results in experiments on a variety of datasets. The experimental results show that the proposed algorithm has larger external indicators of clustering performance metrics, which means that the accuracy of clustering results is significantly improved.

K-means clustering algorithm is an unsupervised learning method with simple principles, easy implementation, and strong adaptability. Aiming at the disadvantages of this algorithm that the clusters’ number is difficult to determine, sensitive to the initial cluster center, and the clustering result is easily impacted by the outliers, this paper proposes an improved clustering algorithm based on density selection, which compares the neighborhood density of each sample and the average density of all the samples, treats the samples with lower density as the outliers or isolated points, and then deletes them. After data pre-processing, the cluster validity index is modified to obtain the optimal clusters’ number by minimizing the cluster validity index, and then optimizes the initial cluster center by density selection strategy. Finally, it is verified by the experiment that the improved algorithm has better accuracy than the traditional K-means algorithm, and it can converge to the global minimum of SSE faster.

With the wide application of GIS to all kinds of fields, and developing of the technique of data mining and spatial data collection, the technique of data mining in spatial database-spatial data mining is coming out. In order to satisfy the people's demand for the interesting and potentially useful knowledge from the spatial database, this thesis used a wide using spatial clustering algorithm: k-means algorithm to discover interesting and potentially useful spatial patterns embedded in spatial database, and also has realized an improved genetic algorithm based on the k-means algorithm. The improved genetic algorithm not only have the global search advantage of genetic algorithm, but also have the feature of local convergence fast of k-means algorithm, meanwhile, it overcome the sensitive to the initial election data and easily fall into the local optimal drawback by traditional k-means algorithm and also raise the convergence rate.