Abstract
Nowadays a greater number of users participates is used to create new issues and discussion on social media that form into different kinds of groups such as Positive and negative comments. This paper is focused on a group of user’s discussions and specifies from which category they belong to. The user messages are parsed in the social media data then they identified network relationships and applied the data mining techniques to a group of different types of communities. The collection of objects is considered as similar or non-similar Clustering structures in unsupervised approaches. The aim of this paper is to develop clusters depending on features and their characteristics that are included in the proposed model. This work helps the system to categorize people into groups which also helps to identify people groups that are participated in discussions. This paper shows the clustering algorithms like K-Means, DBSCAN, and Agglomerative to cluster data and to find large streams of clustering community messages in social media data. This paper throws light on a novel use-case of communities and a proposed algorithm that shows the best clustering results. This application tells us which group of people saw the post and who gave their opinions on the post; by this, we can categorize the users.
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Kolipaka, V.N.S.S.P., Ghanta, N.D.J.R., Koppaka, H.D.P., Pellakuri, V., Haran Babu, P. (2023). Development and Validation of Unsupervised Machine Learning Clustering Techniques. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SMART 2023. Lecture Notes in Networks and Systems, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-99-0769-4_67
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DOI: https://doi.org/10.1007/978-981-99-0769-4_67
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