WiMi Hologram Cloud Develops ANN-Based Data Mining and Clustering Algorithm System
WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider, announced the development of an ANN (artificial neural network) based data mining and clustering optimization algorithm system. In clustering analysis, data is divided into classes according to specific rules, so there is less similarity between types and more within categories. The data analysis results reveal the intrinsic connections and differences between the data and provide an essential basis for further data analysis and knowledge discovery.+
WiMi’s ANN-based data mining and clustering optimization algorithms contain the following methods.
(1) Partitioning: This method finds spherically mutually exclusive clusters, with the centers of the clusters expressed as means or centroids. This algorithm is suitable for clustering problems with a fixed number of clusters and small data sets. The random search strategy makes large-scale data clustering efficient and well-scalable. Partitioned clustering algorithms can be easily parallelized and have been very active on big data processing platforms in recent years.
(2) Hierarchical: This method is based on hierarchical decomposition clustering, which performs a hierarchical decomposition based on the similarity between data points to generate nested clustering trees with a hierarchical structure. The bottom-up hierarchical decomposition corresponds to the coalescent method, while the top-down one corresponds to the split method.
(3) Density-based: This algorithm finds clusters with different shapes without forcing the shape of the clusters to change. It is suitable for clusters with irregular numbers and random shapes and can reduce or even eliminate noise. It divides regions with sufficient density into clusters and finds clusters of arbitrary shapes in noisy spatial databases. It defines clusters as the most extensive set of points with connected density based on the local density of sampled points.
(4) Grid-based: This algorithm clusters the quantified grid space, which is fast and computationally powerful. The space is divided into multiple grids, and the data on the grid is analyzed.
(5) Model clustering: This algorithm assumes that the data is mixed according to a specific probability distribution that works to find the best fit between the data and a given model.
In this era of massive data, data mining is crucial, and its applications are becoming widespread with increasing importance. Companies with a data warehouse or database with analytical value and needs can carry out purposeful data mining to obtain valuable data.
The choice of the clustering method directly determines the quality of data mining, as clustering optimization algorithms can handle data with multidimensional and uncorrelated characteristics. People are constantly searching for better clustering analysis methods to improve the quality of clustering.
The ANN-based data mining clustering and optimization algorithm developed by WiMi can automatically merge clustering results with smaller granularity based on pre-defined warning values, thus effectively preventing the occurrence of narrow clustering results due to the excessive number of specified clusters. With its highly non-linear learning capability, fault tolerance for noisy data, and strong ability to extract rule-based knowledge, the ANN model is superior for data processing and knowledge mining.
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