Author(s): G. Senthil Velan, K. Somasundaram, V.N Rajavarman
There are many methods to alter the data flow theoretically and operationally. When new and extra features are introduced to the stream, or when the significance and relevance of a feature changes as the stream proceeds, a feature-level shift happens. This kind of shift has not garnered as much attention as conceptual alterations. Several clustering techniques (including density, drawing, and grid methods) utilise some kind of distance as a similarity metric, which is problematic with high-dimensional data, since the curse of dimensionality may lead to distance measurement, and any The ideas are extremely tough. calculate. We propose merging them and rephrasing them as feature selection issues, or more specifically dynamic feature selection problems, rather than attempting to answer each of these problems separately. We suggest utilising dynamic feature masks that vary over time to categorise big data streams. n Take action to group similar characteristics that have not been protected. When the perceived significance of characteristics changes, the mask will be changed to reflect the change; before that, smaller features will be unmasked, and comparable features will be disguised as required. In addition, the suggested technique is versatile and may be utilised with all popular density clustering methods, which lack a drift response mechanism and are impacted when dealing with huge volumes. Two texts and two picture sequences are utilised for assessment. The suggested dynamic function mask enhances the efficiency of grouping in all directions and lowers the processing time needed by the basic approach.