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Browsing Local Conference on Parallel and Soft Computing by Subject "Data Mining"

Browsing Local Conference on Parallel and Soft Computing by Subject "Data Mining"

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  • Analysis of Book Sales Transaction using Association Rules Mining 
    Zin, Ei Phyu; Aye, Nilar (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Data Mining is the process of analysis of raw data in the database and synthesizing it into information that is useful for effective decision making. Association rule mining finds interesting association or correlation ...
  • An Approach to CRM: Usage Behavior Analysis with K-Means Algorithm 
    Kyaw, May Thu; Lwin, Mar Mar (Fifth Local Conference on Parallel and Soft Computing, 2010-12-16)
    Nowadays, information and communication technologies are developing with great speed. In the advent of information era, information technology has developed rapidly and has become significant for every business, particularly ...
  • Attribute-Oriented Induction based Data Generalization for Relational Databases 
    Oo, Zin May; Phyu, Sabai (Fifth Local Conference on Parallel and Soft Computing, 2010-12-16)
    Data Mining is the task of discovering interesting patterns from large amounts of data where the data can be stored in relational databases. Data generalization is a process that abstracts a large set of data in a ...
  • Classification of Publication Papers by Using K-nearest Neighbor Algorithm 
    Tun, May Zin (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Classification is a data mining or machine learning technique used to predict group membership for data instances. Several major kinds of classification method including decision tree induction method, Bayesian networks ...
  • Clustering XML Document Based On Path Similarities Using Structure Only 
    Mon, Ei Ei; Tun, Khin Nwe Ni (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    We propose a methodology for clustering XML documents on the basis of their structural similarities. This research combines the methods of common XPath and K-means clustering that improve the efficiency for those XML ...
  • Developing Decision Making System to Classify Pests of Paddy Infesting 
    Aung, Su Mon; Htwe, Tin Tin (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Decision tree learning algorithms have been successfully used in knowledge discovery. These algorithms use induction in order to provide an appropriate classification of objects in terms of their attributes, inferring ...
  • Extraction of Frequent Patterns from Diabetes Cluster 
    Win, Myint Swe Lai; Phyu, Win Lei Lei (Fifth Local Conference on Parallel and Soft Computing, 2010-12-16)
    Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other ...
  • Implementation of Pharmacy Sale System Using Frequent Pattern Growth Algorithm 
    Win, Aye Mya Mya; Mar, Win (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Frequent Pattern mining is played an essential role in data mining. In this system proposes to implement Pharmacy Sale System (PSS) by using Frequent Pattern Growth (FP-growth) Algorithm. This system can manipulate how ...
  • Implementation of Sales Analysis System for Fancy Shop based on Association Rule Mining 
    Oo, Khine Zin; Khaing, Aye Aye (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process ...
  • Implementation of Sequential Pattern Mining with Item Intervals 
    Kyaw, Kay Zar; Hla, Ni Ni (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Sequential Pattern mining is an important data mining field with wide range of applications that can extract frequent sequences while maintaining their order. It is important to identify item intervals of sequential patterns ...
  • Iris Classification using Agglomerative Hierarchical Clustering Approach 
    Swe, Hnin Hnin (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    One of the usages of cluster analysis is to understand unknown data, given the value of several selected features. There are a lot of methods have been proposed data understanding. This paper discusses one technique of ...
  • Malaria Diagnosis System by Using ID3 Classification Algorithm 
    Thuai, Khaing Mar; Thant, Moe (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Decision Tree algorithms are the most popular algorithms for classification in data mining field. The main goal of classification is prediction of the categorical labels (classes). In this system, ID3 algorithm is used to ...
  • Mining Association Rule by ECLAT Method Using Transaction Data 
    Mon, Pan Myat; Renu; Oo, Thet Lwin (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    Association rule mining is a process that identifies links between sets of correlated objects in transactional databases where each transaction contains a list of items. Association rule is one of the well-defined algorithms, ...
  • Prediction of Significant Heart Attack Patterns Using Clustering Algorithm 
    Han, Aye Mya (Fourth Local Conference on Parallel and Soft Computing, 2009-12-30)
    This system presents an efficient approach for discovering significant patterns from the heart disease database for heart attack prediction. The heart disease data warehouse is clustered using Kmeans clustering algorithm ...

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