Possibility Clustering Algorithm for Incomplete Data Based on a Deep Computing Model

Clustering is an essential part of data analytics and in Wireless Sensor Networks (WSN). It becomes a problem for causes such as insufficient, unavailable, or compromised data in the face of uncertainties. A solution to tackle the instability of clusters due to missed values has been proposed. The fundamental theory determines whether to incorporate an entity into a group if it is not clear and probable. One of the main issues is identifying requirements for three forms of decision definition, including an entity in a cluster, removing an object from a group, or delaying a decision (defer) to involve or rule out a group. Current studies do not adequately discuss threshold identification and use their fixed values implicitly. This work explores using the game theory-based Possibility Clustering Algorithm for Incomplete Data (PCA-ID) framework to address this problem. In specific, a game theory will be described in which thresholds are determined based on a balance between the groups' precision and generic characteristics. The points calculated are used to elicit judgments for the grouping of unknown objects. Experimental findings on the deep learning datasets show that the PCA-ID increases the overall quality considerably while maintaining comparable precision levels in competition with similar systems.