Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Nagwani, Naresh Kumar
- A Comparative Study of Software Bug Clustering Using Lingo and STC Web Clustering Algorithms
Abstract Views :394 |
PDF Views:4
Authors
Affiliations
1 National Institute of Technology, Raipur-492001, CG, IN
1 National Institute of Technology, Raipur-492001, CG, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 13 (2011), Pagination: 793-802Abstract
Software bug classification is one of the important and popular problems in software engineering. Recently number of algorithms and techniques are presented to automate this process. Software bug data contains number of attributes like bug-id, summary (title), description, comments, status, version etc. Most of the important attributes holds text data. Lingo and STC (Suffix Tree Clustering) both are popular text clustering algorithms used in web mining. In this paper Lingo and STC algorithms are used to classify the software bugs. Classification using clustering methodology is used to create the software bug classes from software bug clusters. In this methodology first clusters are created and then appropriate labels are assigned to the clusters, which indicate the class label for the clusters. Both of these algorithms Lingo and STC are implemented as the part of Carrot2 framework. The software bug repository data is integrated and passed to Carrot2 framework for applying Lingo and STC algorithms. Lingo and STC algorithms are compared for software bug classification task. The comparison is done using various clustering parameters: the number of clusters generated, purity of the clusters and entropy of the clusters created etc.Keywords
Software Bug Classification, Lingo Clustering, STC Clustering, Software Bug Clustering, Software Bug Repository.
- TARPIN:Discovering Temporal Association Rules Using P-Tree Based Incremental Algorithm
Abstract Views :194 |
PDF Views:2
A new pattern tree algorithm for mining temporal association rules in databases is introduced. This algorithm uses P-Tree (Pattern-Trees) structures for finding temporal association rules in databases. According to different time periods associated with transactions in temporal databases, it will initiate the number of P-Trees and according to time information in transactions it inserts the transactions in created appropriate trees, then using P-Tree association rule mining algorithm it finds out the frequent sets in this P-Tree and then these frequent items are merged with different time periods which will give the association rules with valid time periods. The proposed algorithm is divided in two phases in first phase all item within the transactions are inserted in different P-Trees on which the frequent item-sets are taken out and in second (merge phase) these frequent items are merged and time associates with these items are in listed which indicates that these frequent items are frequent in this time periods. Algorithm is implemented in C++ under Linux platform and evaluated results are compared with existing popular algorithm PPM (Progressive Partition Miner) for discovering temporal association rule.
Authors
Affiliations
1 National Institute of Technology, Raipur-492001, CG, IN
1 National Institute of Technology, Raipur-492001, CG, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 8 (2009), Pagination: 392-404Abstract
Association rule mining is one of very popular data mining method and number of organizations uses this technique to find the frequent item-sets of products to improve the benefits of organizations. There are number of available algorithms for association rule mining which takes multiple scans of database. The complexities of association rule algorithms primarily depends on number of database scan, so by reducing the number of database scans one can improve the time complexity of these algorithms. The purpose of this proposed algorithm is to reduce the number of database scans for discovering the temporal association rules by applying P-Tree algorithm for temporal association rules which takes just one scan of database to find out the association rules.A new pattern tree algorithm for mining temporal association rules in databases is introduced. This algorithm uses P-Tree (Pattern-Trees) structures for finding temporal association rules in databases. According to different time periods associated with transactions in temporal databases, it will initiate the number of P-Trees and according to time information in transactions it inserts the transactions in created appropriate trees, then using P-Tree association rule mining algorithm it finds out the frequent sets in this P-Tree and then these frequent items are merged with different time periods which will give the association rules with valid time periods. The proposed algorithm is divided in two phases in first phase all item within the transactions are inserted in different P-Trees on which the frequent item-sets are taken out and in second (merge phase) these frequent items are merged and time associates with these items are in listed which indicates that these frequent items are frequent in this time periods. Algorithm is implemented in C++ under Linux platform and evaluated results are compared with existing popular algorithm PPM (Progressive Partition Miner) for discovering temporal association rule.