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Top 10 algorithms in data mining
, 2007
"... Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, kMeans, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining a ..."
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Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, kMeans, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification,
Markov Chain based Emissions Models: a Precursor for Green Control
"... In this chapter we propose a new method of modelling urban pollutants arising from transportation networks. The efficacy of the proposed approach is demonstrated by means of a number of examples. Our models give rise to a number of surprising observations that are relevant for the regulation of poll ..."
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In this chapter we propose a new method of modelling urban pollutants arising from transportation networks. The efficacy of the proposed approach is demonstrated by means of a number of examples. Our models give rise to a number of surprising observations that are relevant for the regulation of pollution in urban networks: Different actions are required for the control of different pollutants and low speed limits do not necessarily lead to low pollution.
METHODOLOGY Optimizing
"... drug–target interaction prediction based on random walk on heterogeneous networks ..."
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drug–target interaction prediction based on random walk on heterogeneous networks
RESEARCH ARTICLE Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language Editions
"... Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their article ..."
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Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. We also obtain the distributions of historical figures over world countries, highlighting geographical aspects of crosscultural links. Considering historical figures who appear in multiple editions as interactions between cultures, we construct a network of cultures and identify the most influential cultures according to this network.
DOI 10.1007/s1011500701142 SURVEY PAPER Top 10 algorithms in data mining
, 2007
"... Abstract This paper presents the top 10 data mining algorithms identified by the IEEE ..."
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Abstract This paper presents the top 10 data mining algorithms identified by the IEEE
Social Networks based eLearning Systems via Review of Recommender Systems Techniques
"... eLearning has turned to be a necessity for everyone, as it enables continuous and lifelong education. Learners are social by nature. They want to connect to othersand share the same interests. Online communities are important to help and encourage learners to continue education. Learners through s ..."
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eLearning has turned to be a necessity for everyone, as it enables continuous and lifelong education. Learners are social by nature. They want to connect to othersand share the same interests. Online communities are important to help and encourage learners to continue education. Learners through social capabilities can share different experiences.Social networks are cornerstone for eLearning. However, alternatives are many. Learners might get lost in the tremendous learning resources that are available. It is the role of recommender systems to help learners find their own way through eLearning. We present a review of different recommender system algorithms that are utilized in social networks based eLearning systems. Future research will include our proposed our eLearning system that utilizes Recommender System and Social Network.
Highlighting entanglement of cultures via ranking of multilingual Wikipedia articles
, 2013
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1Google matrix analysis of DNA sequences
"... For DNA sequences of various species we construct the Google matrix G of Markov transitions between nearby words composed of several letters. The statistical distribution of matrix elements of this matrix is shown to be described by a power law with the exponent being close to those of outgoing lin ..."
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For DNA sequences of various species we construct the Google matrix G of Markov transitions between nearby words composed of several letters. The statistical distribution of matrix elements of this matrix is shown to be described by a power law with the exponent being close to those of outgoing links in such scalefree networks as the World Wide Web (WWW). At the same time the sum of ingoing matrix elements is characterized by the exponent being significantly larger than those typical for WWW networks. This results in a slow algebraic decay of the PageRank probability determined by the distribution of ingoing elements. The spectrum of G is characterized by a large gap leading to a rapid relaxation process on the DNA sequence networks. We introduce the PageRank proximity correlator between different species which determines their statistical similarity from the view point of Markov chains. The properties of other eigenstates of the Google matrix are also discussed. Our results establish scalefree features of DNA sequence networks showing their similarities and distinctions with the WWW and linguistic networks.
Google Matrix Analysis of DNA Sequences
"... For DNA sequences of various species we construct the Google matrix G of Markov transitions between nearby words composed of several letters. The statistical distribution of matrix elements of this matrix is shown to be described by a power law with the exponent being close to those of outgoing link ..."
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For DNA sequences of various species we construct the Google matrix G of Markov transitions between nearby words composed of several letters. The statistical distribution of matrix elements of this matrix is shown to be described by a power law with the exponent being close to those of outgoing links in such scalefree networks as the World Wide Web (WWW). At the same time the sum of ingoing matrix elements is characterized by the exponent being significantly larger than those typical for WWW networks. This results in a slow algebraic decay of the PageRank probability determined by the distribution of ingoing elements. The spectrum of G is characterized by a large gap leading to a rapid relaxation process on the DNA sequence networks. We introduce the PageRank proximity correlator between different species which determines their statistical similarity from the view point of Markov chains. The properties of other eigenstates of the Google matrix are also discussed. Our results establish scalefree features of DNA sequence networks showing their similarities and distinctions with the WWW and linguistic networks.