ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING

Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members.

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

Component-based Access Design Method for Dynamic Spatial Database

In order to realize the optimal access of dynamic spatial database, a component-based optimal access method of dynamic spatial database is proposed. The statistical information distribution model for storing the characteristic data of association rules is constructed in the dynamic spatial database. The fuzzy information features are extracted by using the dynamic component fusion clustering analysis method. Combined with the distributed association feature quantity, the fusion scheduling is carried out to control the dynamic information clustering. Combined with fuzzy c-means clustering analysis method, dynamic attribute classification analysis is carried out. The dynamic component block matching model is used for update iterative optimization, and the optimal access to the dynamic spatial database is realized in the cluster center. Simulation results show that this method has strong adaptability to the optimal access of dynamic spatial database, and has high accuracy and good convergence for data information extraction in dynamic spatial database.

The Distance and Dynamical History of the Virgo Cluster Ultradiffuse Galaxy VCC 615

We use deep Hubble Space Telescope imaging to derive a distance to the Virgo Cluster ultradiffuse galaxy (UDG) VCC 615 using the tip of the red giant branch (TRGB) distance estimator. We detect 5023 stars within the galaxy, down to a 50% completeness limit of F814W ≈ 28.0, using counts in the surrounding field to correct for contamination due to background sources and Virgo intracluster stars. We derive an extinction-corrected F814W tip magnitude of m tip , 0 = 27.19 − 0.05 + 0.07 , yielding a distance of d = 17.7 − 0.4 + 0.6 Mpc. This places VCC 615 on the far side of the Virgo Cluster (d Virgo = 16.5 Mpc), at a Virgocentric distance of 1.3 Mpc and near the virial radius of the main body of Virgo. Coupling this distance with the galaxy’s observed radial velocity, we find that VCC 615 is on an outbound trajectory, having survived a recent passage through the inner parts of the cluster. Indeed, our orbit modeling gives a 50% chance the galaxy passed inside the Virgo core (r < 620 kpc) within the past gigayear, although very close passages directly through the cluster center (r < 200 kpc) are unlikely. Given VCC 615's undisturbed morphology, we argue that the galaxy has experienced no recent and sudden transformation into a UDG due to the cluster potential, but rather is a long-lived UDG whose relatively wide orbit and large dynamical mass protect it from stripping and destruction by the Virgo cluster tides. Finally, we also describe the serendipitous discovery of a nearby Virgo dwarf galaxy projected 90″ (7.2 kpc) away from VCC 615.

A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data

The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibilistic Fuzzy C-Means (ImPFCM) by combining the PFCM algorithm with a very powerful statistical method. The properties of this new ImPFCM algorithm show that it is not only applicable on clusters of spherical shapes, but also on clusters of different sizes and densities. The results of the comparative study with very recent algorithms indicate the performance and the superiority of the proposed approach to easily group the datasets in a large-dimensional space and to use not only the Euclidean distance but more sophisticated standards norms, capable to deal with much more complicated problems. On the other hand, we have demonstrated that the ImPFCM algorithm is also capable of detecting the cluster center with high accuracy and performing satisfactorily in multiple environments with noisy data and outliers.

Service Partition Method Based on Particle Swarm Fuzzy Clustering

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

THE POSSIBILITIES OF THE “CLUSTER CARTOGRAPHY” TOOL FOR THE STUDY OF THE INNER STRUCTURES OF GALAXY CLUSTERS

We describe the possibilities of the “Cluster Cartography” tool which was created for detailed study of the 2D distribution of galaxies in the clusters. The main tasks of the “Cluster Cartography” tool were the detailed study of the morphologyof galaxy clusters using the statistically significant numerical criteria as well as to detect their regular peculiarities. The tool allows to create the 2D map with positions of galaxies in the cluster field and show for each cluster member its shape and orientation as a best-fit ellipse using input catalogue data. The size of symbols for galaxies correspond to input data.It may reflect the galaxy image in arcseconds from catalogue in the map 4000×4000arcsec. Another way connects the size of the symbol with the magnitude of the galaxy. Tool is able to build the map in four modes: the symbols are dots; the symbols are circles with diameters reflected the magnitudes of galaxies; the symbols are ellipses with size reflected the magnitudesand both ellipticities and orientation from the input catalogue; the symbols illustrate the shape of galaxies in projection to the celestial sphere. The “Cluster Cartography” algorithms allow to detect the standard cases in galaxy distribution, suchas the degree of concentration to the cluster center and/or to some line on a statistically significant level using the numerical criteria. Also “Cluster Cartography” allows to detect other features, such as crosses, semi-crosses, complex crosses and short compact chains, as well as to export the list of galaxies forming the peculiarities for the futurestudy. The final version of the “Cluster Cartography” allows to realize the modern scheme for detailed morphological classification of galaxy clusters. The “Cluster Cartography” is powerful and perspective tool for study of features of galaxy clusters.

Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm

Based on the parallel K-means algorithm, this article conducts in-depth research on the related issues of marketing node detection under the Internet, including designing a new Internet marketing node detector and a location summary network based on FCN (Full Convolutional Network) to input the preprocessing of the node and verify its performance under the data sets. At the same time, to solve the problem of insufficient data sets of Internet marketing nodes, the Internet data sets are artificially generated and used for detector training. First, the multiclass K-means algorithm is changed to two categories suitable for Internet marketing node detection: marketing nodes and background categories. Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. Therefore, when processing Internet marketing node detection tasks, the K-means algorithm is used to regress the training set and calculate 5 weights. During the simulation experiment, the weight calculation formula is used to calculate the weight of the feature term. The basic idea is that if a feature word appears more often in this document but less frequently in other nodes, the word will be assigned higher. At the same time, this article focuses on k. Some shortcomings of the mean clustering algorithm have been specifically improved. By standardizing the data participating in the clustering, the data participating in the clustering is transformed from an irregular distribution to a cluster-like distribution, thereby facilitating the clustering process. The density is introduced to determine the initial center of the cluster, and the purity metric is introduced to determine the appropriate density radius of the cluster center, to achieve the most effective reduction of the support vector machine training samples.

Research on Density-Based K-means Clustering Algorithm

Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.

A Study of Data-Driven Enterprise Human Resource Management Model

With the deepening of diversified and professional business philosophy in large enterprises, enterprises play more and more abundant social functions in society. Sense of mission and honor play an important role in the healthy development of enterprises. With the in-depth integration of enterprise management and computer technology, some human resource management problems related to corporate social responsibility often appear in this process. From the traditional human resource management paradigm to people-oriented social responsibility human resource management, “strengthening effectiveness” has become the main research direction of enterprise management in the electronic age. On this basis, through the discrete modeling method of a large amount of data, this paper creatively puts forward the coupling correlation between corporate social responsibility and enterprise human resource management based on grey correlation algorithm. Compared with the management mode based on trapezoidal data analysis and cluster center adopted in the current mainstream enterprise human resource management research, the innovation of this algorithm is to analyze the dynamic data of corporate social responsibility and enterprise human resources and establish a coupling model related to corporate social responsibility. It can not only realize the dynamic tracking of human resource data, but also make full use of the relevant characteristic information of the coupling relationship between enterprise human resource management and corporate social responsibility. According to the dynamic big data such as employee welfare, employee compensation, employee training, employee overtime, and timeliness of transaction processing, this paper analyzes the social problems such as corporate social responsibility evaluation index, employee turnover rate, and enterprise income, so as to provide theoretical support for enterprise business strategy.