Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.
With 3.8 billion users, social media created ethical problems as well. The Cambridge Analytica scandal has been a serious issue with data security lately. Contents not in compliance with general moral rules is another important violation of ethics. TikTok application, the fastest-rising short video-sharing website, is examined. As a result of the literature review, it is observed that TikTok application also had ethical violations issues like lack of private data safety, not sufficient precautionary system barriers for the young generation, and addiction risk. Furthermore, considering that the young people can interact with malicious users through the fake accounts and the risk of sharing their exceptional videos to have more viewers, it is suggested that the sanctions should be arranged as a deterrent in violation of the rules. Parents and young users must be educated about the risks and ethical violations of social media.
Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC