scholarly journals FANATIC: FAst Noise-Aware TopIc Clustering

Author(s):  
Ari Silburt ◽  
Anja Subasic ◽  
Evan Thompson ◽  
Carmeline Dsilva ◽  
Tarec Fares
Keyword(s):  
Computation ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 34 ◽  
Author(s):  
Georgios Drakopoulos ◽  
Andreas Kanavos ◽  
Ioannis Karydis ◽  
Spyros Sioutas ◽  
Aristidis G. Vrahatis
Keyword(s):  

2013 ◽  
Author(s):  
Yongheng Wang ◽  
Kening Cao ◽  
Xiaoming Zhang

2017 ◽  
Vol 18 (2) ◽  
pp. 128-138 ◽  
Author(s):  
Tariq Taleb ◽  
Mejdi Kaddour

Abstract Extending the lifetime of wireless sensor networks (WSNs) while delivering the expected level of service remains a hot research topic. Clustering has been identified in the literature as one of the primary means to save communication energy. In this paper, we argue that hierarchical agglomerative clustering (HAC) provides a suitable foundation for designing highly energy efficient communication protocols for WSNs. To this end, we study a new mechanism for selecting cluster heads (CHs) based both on the physical location of the sensors and their residual energy. Furthermore, we study different patterns of communications between the CHs and the base station depending on the possible transmission ranges and the ability of the sensors to act as traffic relays. Simulation results show that our proposed clustering and communication schemes outperform well-knows existing approaches by comfortable margins. In particular, networks lifetime is increased by more than 60% compared to LEACH and HEED, and by more than 30% compared to K-means clustering.


2013 ◽  
Vol 433-435 ◽  
pp. 626-629
Author(s):  
Hong Xin Wan ◽  
Yun Peng

The discovery of public opinion hotspot is an important aspect of public opinion research, and because many similarities and relevance exist between hot topics, we propose a hot topic clustering algorithm to find the hotspot in public opinions. Since fuzzy set can handle non-precision data well, the fuzzy algorithm can reduce the influences of the uncertainty of public opinion data. Based on LDA topic extraction we cluster the topical words by fuzzy method, and take the topic probability as word membership to the cluster. It can reduce the noise data and improve the ability of hotspot discovery that aggregate the similar and related topic to one class. The topical key words with high probability in cluster are the hotspot, and singular cluster with few words can be looked as outlier. The algorithm is demonstrated by example analysis in detail.


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