Browsing by Subject "Decision tree"
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Item Analysis of Queries Routing in Super-Super-Peer Based P2P Architecture Using NBTree: The Hybrid Algorithm(Department of Computer Science and Information Technology, 2014) Dewan, AbhishekThe Internet is converging to a more dynamic, huge, fully distributed peer-to-peer (P2P) overlay networks containing millions of nodes typically for the purpose of information distribution and file sharing as the increase in the number of computers connected to the Internet are increasing rapidly. Because of which a challenging problem in unstructured P2P system is how to locate peers that are relevant with respect to a given query with minimum query processing and minimum answering time. Connected peers can leave the overlay network any time and new peers can join it any time. To achieve our goal we suggest an unstructured P2P system which is based on an organization of peers around super-peers that is connected to super-superpeer according to their semantic domains and also uses NBTree: The Hybrid Algorithm to extract Super-Peer that contains peers with relevant data respect to a given query. Keywords: Decision Tree, Machine Learning, NBTree, P2P, P2P Queries Answering, P2P Queries Routing, Super-Super-Peer, WekaItem Comparative Analysis of Decision Tree Classification Algorithms(Department of Computer Science and Information Technology, 2014) Karmachrya, ShikhaIn our daily life there is lots of records, phone call records, salary records, homework records, assignment record, personal details record, sales record, song, videos and so on. These all records kept in a table are called data; we have lots of data in different field. Whenever there is data we can have lots of information, patterns, meaning etc. Data mining applications has got rich focus due to its significance of classification algorithms. The comparison of classification algorithm is a complex and it is an open problem. First, the notion of the performance can be defined in many ways: accuracy, speed, cost, reliability, etc. Second, an appropriate tool is necessary to quantify this performance. Third, a consistent method must be selected to compare with the measured values. The selection of the best classification algorithm for a given dataset is a very widespread problem. In this sense it requires to make several methodological choices. So this research focused in the analysis of decision tree classification algorithm in different datasets of multiple attributes and multiple instances. Where analysis was done among five decision tree algorithms (BFTree, J48, Random Tree, REP Tree and Simple Cart).J 48 was able to classify 82.16% of the data correctly which was best among all in comparison to results of evaluation metrics (Accuracy, Precision, Recall and F-Measure) and Simple Cart was able to build decision tree with small tree size of 17.24 (averaged value). Keywords: BF Tree,CART, Data Mining, Decision Tree, J48,Random Tree, REP Tree.