A Effective Social media community using optimized clustering algorithm



A Effective Social media community using optimized clustering algorithm

         Prachi malviya1                          Pyuish rai2



Abstract :- Now-a-days social media is employed to introduce new problems and discussion on social media. a lot of variety of users participates in discussion via social media. totally different completely different  users belong to different quite teams. Positive and negative comments are going to be announce by the user and that they can participate in discussion . Here we have a tendency to planned a system to cluster completely different quite users and system specifies from that class they belong to. Once the social media knowledge like user messages are parsed and network relationships are known, data processing techniques will be applied to cluster of various sorts of communities. we have a tendency to used K-Means bunch algorithmic program to cluster knowledge. during this system we have a tendency to discover communities by bunch messages from massive streams of social knowledge. Our planned algorithmic program provides higher bunch results and provides a unique use-case of grouping user communities supported their activities. This application is employed to spot cluster of individuals World Health Organization viewed the post and commented on the post.

Keyword :- Data mining , clustering algorithm , feature extraction , k- mean cluster



 Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD) an interdisciplinary subfield of science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspect data pre-processing, model an inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.  The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" or when referring to actual methods, artificial intelligence and machine learning are more appropriate.  The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices.

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