An ANN (artificial neural network) based data mining and clustering optimization algorithm system has been developed, according to WiMi Hologram Cloud Inc., a major global provider of hologram augmented reality (“AR”) technology. When data is categorized using precise principles, such as clustering analysis, there is less similarity across kinds and greater similarity within categories. The findings from the data analysis provide a crucial foundation for additional data analysis and knowledge discovery by highlighting the natural connections and contrasts between the data.
The following techniques are included in WiMi’s ANN-based data mining and clustering optimization algorithms.
(1) Partitioning: The centers of the clusters are represented as means or centroids, and the approach finds spherically mutually exclusive clusters. Little data sets and clustering issues with a fixed number of clusters are good candidates for this approach. The large-scale data clustering process is effective and well-scalable thanks to the random search approach. Algorithms for partitioned clustering are simple to parallelize and have seen a lot of use recently on large data processing systems.
(2) Hierarchical: This approach is based on hierarchical decomposition clustering, which creates layered clustering trees with a hierarchical structure by performing a hierarchical decomposition based on the similarity between data points. The split approach corresponds to the top-down hierarchical decomposition, whereas the coalescent method relates to the bottom-up one.
(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 approach, which is quick and powerful computationally, clusters the quantized grid space. Several grids are placed throughout the area, and the data on each grid is examined.
(5) Model clustering: This technique looks for the best match between the data and a certain model by assuming that the data is mixed according to a particular probability distribution.
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.
Due to the fact that clustering optimization techniques can handle data with multidimensional and uncorrelated properties, the choice of the clustering method directly affects the quality of data mining. To enhance the quality of clustering, people are continually looking for improved clustering analysis techniques.
By automatically merging clustering results with lesser granularity based on pre-defined warning values, the ANN-based data mining clustering and optimization method created by WiMi efficiently prevents the formation of narrow clustering results owing to an excessive number of specified clusters. The ANN model is excellent for data processing and knowledge mining due to its extremely non-linear learning power, fault tolerance for noisy data, and great ability to extract rule-based information.
