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GLOBALSOFT TECHNOLOGIES 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com 
A Two-Level Topic Model Towards Knowledge 
Discovery from Citation Networks 
Abstract 
Knowledge discovery from scientific articles has received increasing 
attention recently since huge repositories are made available by the 
development of the Internet and digital databases. In a corpus of 
scientific articles such as a digital library, documents are connected by 
citations and one document plays two different roles in the corpus: 
document itself and a citation of other documents. In the existing topic 
models, little effort is made to differentiate these two roles. We believe 
that the topic distributions of these two roles are different and related in 
a certain way. In this paper, we propose a Bernoulli process topic (BPT) 
model which considers the corpus at two levels: document level and 
citation level. In the BPT model, each document has two different 
representations in the latent topic space associated with its roles. 
Moreover, the multi-level hierarchical structure of citation network is 
captured by a generative process involving a Bernoulli process. The 
distribution parameters of the BPT model are estimated by a variational 
approximation approach. An efficient computation algorithm is 
proposed to overcome the difficulty of matrix inverse operation. In 
addition to conducting the experimental evaluations on the document
modeling and document clustering tasks, we also apply the BPT model 
to well known corpora to discover the latent topics, recommend 
important citations, detect the trends of various research areas in 
computer science between 1991 and 1998, and to investigate the 
interactions among the research areas. The comparisons against state-of-the- 
art methods demonstrate a very promising performance. The 
implementations and the data sets are available online . 
System Configuration:- 
Hardware Configuration:- 
 Processor - Pentium –IV 
 Speed - 1.1 Ghz 
 RAM - 256 MB(min) 
 Hard Disk - 20 GB 
 Key Board - Standard Windows Keyboard 
 Mouse - Two or Three Button Mouse 
 Monitor - SVGA 
Software Configuration:- 
 Operating System : Windows XP 
 Programming Language : JAVA 
 Java Version : JDK 1.6 & above.

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IEEE 2014 JAVA DATA MINING PROJECTS A two level topic model towards knowledge discovery from citation networks

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks Abstract Knowledge discovery from scientific articles has received increasing attention recently since huge repositories are made available by the development of the Internet and digital databases. In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. In the existing topic models, little effort is made to differentiate these two roles. We believe that the topic distributions of these two roles are different and related in a certain way. In this paper, we propose a Bernoulli process topic (BPT) model which considers the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach. An efficient computation algorithm is proposed to overcome the difficulty of matrix inverse operation. In addition to conducting the experimental evaluations on the document
  • 2. modeling and document clustering tasks, we also apply the BPT model to well known corpora to discover the latent topics, recommend important citations, detect the trends of various research areas in computer science between 1991 and 1998, and to investigate the interactions among the research areas. The comparisons against state-of-the- art methods demonstrate a very promising performance. The implementations and the data sets are available online . System Configuration:- Hardware Configuration:-  Processor - Pentium –IV  Speed - 1.1 Ghz  RAM - 256 MB(min)  Hard Disk - 20 GB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA Software Configuration:-  Operating System : Windows XP  Programming Language : JAVA  Java Version : JDK 1.6 & above.