# A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube
## 2 Literature Review
However, setting the appropriate micro-cluster radius is extremely difficult in practice, and a global radius might not even be optimal for each micro-cluster. A poor choice of radius significantly degrades the clustering quality. To address this issue,i-CODAS updates the radius in real-time to its local optimal value whenever a new data sample is added to the cluster. Metadata, referred to as micro-clusters, is used to summarize the data samples. The micro-clusters are visualized in a clustering graph based on their connectedness. Finally, the clustering graph is utilized to build clusters of any shape. Nonetheless, one of the algorithm's primary drawbacks is its high memory requirement. As an improvement of CEDAS, a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream BOCEDS was introduced by [15]. This technique changes the micro-cluster radius iteratively until it reaches its local optimal value, which was one of the restrictions described on CEDAS. BOCEDS additionally provides a buffer for storing unnecessary micro-clusters and a fully online pruning mechanism for removing the buffer's momentarily irrelevant micro-clusters. Experimental results prove BOCEDS's superiority over the other clustering algorithms. Yet, the study fails to decrease the memory consumption without impairing other clustering performance criteria. Furthermore, the study also failed to identify false merging for overlapped clusters. Similarly, an online incremental clustering framework named OICF-Stream was presented for data stream analytics [21]. It is a strategy that employs an incremental clustering technique to operate within real-time limitations, in real-time, while maintaining accuracy. The experimental results reveal that the proposed strategy is superior in terms of accuracy and has a significant improvement in execution time. However, the created algorithm is incapable of handling multidimensional data streams and so consumes a great deal of processing power. Another online dynamic clustering method for tracking evolving environments called DyClee was proposed by [22]. It is a unified approach to monitoring developing environments that use a two-stage clustering technique based on distance and density. In this approach, data samples are incrementally fed online into the distance-based clustering stage, where they are grouped to generate \(\mu\)-clusters. To generate the final clusters, the density-based algorithm analyses the micro-clusters. Additionally, the approach is capable of recognizing clusters with a high degree of overlap even in multi-density distributions without making any assumptions regarding cluster convexity. The experimental results demonstrate that the suggested approach responds quickly to data streams and has a high rate of outlier rejection. However, the proposed technique does not account for drift data stream clustering. Recently, a new online method called CEDGM was proposed by [23], which is a technique for clustering data streams based on a density grid. The density grid-based technique's key goals are to minimize distant function calls as well as to increase cluster quality. The technique is fully online and is divided into two distinct phases. The first phase is responsible for generating the Core Micro-Clusters (CMCs), while the second phase merges the CMCs to form macro clusters. The grid-based technique was used as an outlier buffer to manage data with varying densities and noise. The developed technique was shown to be an effective solution for minimizing distance function calls and enhancing cluster quality. Nonetheless, one of the CEDGM algorithm's shortcomings when dealing with high-dimensional data is the grid's ineffectiveness at lowering memory usage, particularly for high-dimensional data that are sparse in nature. A recent fully online clustering algorithm for evolving data-stream based on data point density called OCED was proposed by [24].