Clustering proteomics data is a challenging problem for any
traditional clustering algorithm. Usually, the number of samples
is largely smaller than the number of protein peaks. The
use of a clustering algorithm which does not take into
consideration the number of features of variables (here the number
of peaks) is needed. An innovative hierarchical clustering
algorithm may be a good approach. We propose here a new
dissimilarity measure for the hierarchical clustering combined
with a functional data analysis. We present a specific application
of functional data analysis (FDA) to a high-throughput
proteomics study. The high performance of the proposed algorithm
is compared to two popular dissimilarity measures in the
clustering of normal and human T-cell leukemia virus type 1
(HTLV-1)-infected patients samples.
Social networking sites will attract millions of users around the globe. Internet media is becoming popular for news consumption because of its ease, simple access and fast spreading of data takes to consume news from social media. Fake news on social media is making an appearance that is attracting a huge attention. This kind of situation could bring a great conflict in real time. The false news impacts extremely negative on society, particularly in social, commercial, political world, also on individuals. Hence detection of fake news on social media became one of the emerging research topic and technically challenging task due to availability of tools on social media. In this paper various machine learning techniques are used to predict fake news on twitter data. The results shown by using these techniques are more accurate with better performance.
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