Third Local Conference on Parallel and Soft Computing
https://onlineresource.ucsy.edu.mm/handle/123456789/682
2024-03-29T07:20:44ZImplementation of Bagged Classifier Based on Naïve Bayesian Classification
https://onlineresource.ucsy.edu.mm/handle/123456789/2137
Implementation of Bagged Classifier Based on Naïve Bayesian Classification
Oo, Myat Htun; Htun, Myat Thu Zar
Classification is an important data mining technique which predicts the class of a given data sample.Classification allocates new object to one out of a finite set of previously defined classes pm the basis of observations on several characteristics of the objects called attributes(or)features.The accuracy of the performance of classifier can be enhanced by using some techniques.One of these techniques is Bagging.The proposed system intends to implement a bagged classifier based on naïve Bayesian classifications to predict the class label of an unknown sample.The implemented classifier can be used as a supporting tool for decision making problems.The system will use german credit data approval at the bank as a case study.In this system,it will train based on credit data set and show how to get credit information with high accuracy for each customer.
2009-08-03T00:00:00ZSequential Patterns Mining For DNA Sequences Based On Divided and Conquers Approach
https://onlineresource.ucsy.edu.mm/handle/123456789/2136
Sequential Patterns Mining For DNA Sequences Based On Divided and Conquers Approach
Htein, Aung Thet
Data mining is process of pattern extraction from a large collection of datasets.Main goal of data mining is to discover the frequent itemsets(patterns).Sequential pattern mining is an important data mining problem that generates a combinatorial explosive number of intermediate subsequences.Sequential pattern mining generates patterns based on item occurrence order.PrefixlSpan is one of the fast sequential pattern mining algorithms based on divide and conquer approach.PrefixSpan algorithm partitons databases based on currently identified frequent patterns and grow to longer ones using projected databases.This paper presented mining DNA sequential patterns based on divide and conquers approach.Divide and conquer strategy process is partitioning method.By using PrefixSpan method,projected databases are processed in parallel,therefore processing time can be reduced and it will support the bioinformatics field.
2009-08-03T00:00:00ZImplementation of Bagged Classifier Based on Naïve Bayesian Classification
https://onlineresource.ucsy.edu.mm/handle/123456789/2135
Implementation of Bagged Classifier Based on Naïve Bayesian Classification
Oo, Myat Htun; Htun, Myint Thu Zar
Classification is an important data mining technique which predicts the class of a given data sample.Classification allocates new object to one out of a finite set of previously defined classes pm the basis of observations on several characteristics of the objects called attributes(or)features.The accuracy of the performance of classifier can be enhanced by using some techniques.One of these techniques is Bagging.The proposed system intends to implement a bagged classifier based on naïve Bayesian classifications to predict the class label of an unknown sample.The implemented classifier can be used as a supporting tool for decision making problems.The system will use german credit data approval at the bank as a case study.In this system,it will train based on credit data set and show how to get credit information with high accuracy for each customer.
2009-08-03T00:00:00ZClustering Spatial Data using DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
https://onlineresource.ucsy.edu.mm/handle/123456789/2115
Clustering Spatial Data using DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Yan, Phyo Wai; Thida, Aye
Clustering algorithms are data attractive for the last class identification in spatial databases. This system presents the new clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise). DBSCAN is a density-based clustering algorithm, grows regions with sufficiently high density into clusters and discovers of arbitrary shape and size in spatial databases. DBSCAN defines a cluster as a maximum set of density-connected objects. Every object not contained in any cluster is considered to be noise. DBSCAN is efficient even for large spatial databases. This system performs the effectiveness and efficiency of DBSCAN using spatial databases. The results demonstrate that DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLARANS (Clustering Large Applications based on RANdomized Search) and the run time comparison of DBSCAN and CLARANS on these databases in terms of efficiency.
2009-08-03T00:00:00Z