Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features

Devarakonda, Nagaraju; Anandarao, Sarvani; Kamarajugadda, Raviteja; Wang, Yingxu

doi:10.1109/iccicc46617.2019.9146092

Cited by 1 publication

(1 citation statement)

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“…K-means [26][27][28] comes under unsupervised data clustering algorithm. In this initially k-centroids are defined to form kclusters.…”

Section: K-means Clusteringmentioning

confidence: 99%

Detection of Hot Topic in Tweets Using Modified Density Peak Clustering

Anandarao¹,

Chellasamy²

2021

ISI

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Tweets based micro blogging is the most widely used social media to share the opinions in terms of short messages. Tweets facilitate business men to release the products based on the user interest which thereby produces more profits to their business. It also helps the government to monitor the public opinion which leads to better policies and standards. The large number of tweets on different topics are shared daily so, there is a need to identify trending topics. This paper proposes a method for automatic detection of hot topics discussed predominantly in social media by aggregating tweets of similar topics into manageable clusters. This produces hot topic detection irrespective of the current user location. A Modified Density Peak Clustering (MDPC) algorithm based hot topic detection is proposed. Local density of traditional Density Peak Clustering (DPC) is redefined by using the gaussian function in the calculation of dc (threshold distance). The traditional DPC considering some random value as dc (threshold distance) this gives a negative impact on the cluster formation thereby return inappropriate clusters. This can be solved by using the MDPC. The MDPC algorithm works by taking the cosine similarity between the tweets as the input and produces clusters of similar tweets. The cluster having a greater number of tweets is considered as hot topic which is frequently discussed by most of the users on twitter. Events 2012 dataset is collected with streaming API. This contains tweets from 2012 to 2016. The dataset consists of 149 target events and 30 million tweets. Experimental result shows that the proposed algorithm performs better than the traditional algorithms such as density peak clustering, K-means clustering, and Spectral clustering. It has produced the accuracy of 97%.

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“…K-means [26][27][28] comes under unsupervised data clustering algorithm. In this initially k-centroids are defined to form kclusters.…”

Section: K-means Clusteringmentioning

confidence: 99%