Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
Information Processing and Management: an International Journal
Optimal Partitioning for Classification and Regression Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Document clustering for electronic meetings: an experimental comparison of two techniques
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Data mining: concepts and techniques
Data mining: concepts and techniques
A vector space model for automatic indexing
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Decision Support Systems - Special issue: Data mining for financial decision making
Tapping the power of text mining
Communications of the ACM - Privacy and security in highly dynamic systems
Building a scientific knowledge web portal: the NanoPort experience
Decision Support Systems
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
Top 10 algorithms in data mining
Knowledge and Information Systems
Seeding the survey and analysis of research literature with text mining
Expert Systems with Applications: An International Journal
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
A Latent Semantic Indexing-based approach to multilingual document clustering
Decision Support Systems
Machine learning techniques for business blog search and mining
Expert Systems with Applications: An International Journal
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Expert Systems with Applications: An International Journal
Applying decision tree and neural network to increase quality of dermatologic diagnosis
Expert Systems with Applications: An International Journal
Clustering of document collection - A weighting approach
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
A text mining approach for automatic construction of hypertexts
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combining preference- and content-based approaches for improving document clustering effectiveness
Information Processing and Management: an International Journal
Automatic keyphrases extraction from document using neural network
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Media coverage in times of political crisis: A text mining approach
Expert Systems with Applications: An International Journal
Evaluating and understanding text-based stock price prediction models
Information Processing and Management: an International Journal
Hi-index | 12.06 |
As the Internet has been the virtual place where citizens are united and their opinions are promptly shifted into the action, two way communications between the government sector and the citizen have been more important among activities of e-Government. Hence, Anti-corruption and Civil Rights Commission (ACRC) in the Republic of Korea has constructed the online petition portal system named e-People. In addition, the nation's Open Innovation through e-People has gained increasing attention. That is because e-People can be applied for the virtual space where citizens participate in improving the national law and policy by simply filing petitions to e-People as the voice of the nation. However, currently there are problems and challenging issues to be solved until e-People can function as the virtual space for the nation's Open Innovation based on petitions collected from citizens. First, there is no objective and systematic method for analyzing a large number of petitions filed to e-People without a lot of manual works of petition inspectors. Second, e-People is required to forecast the trend of petitions filed to e-People more accurately and quickly than petition inspectors for making a better decision on the national law and policy strategy. Therefore, in this paper, we propose the framework of applying text and data mining techniques not only to analyze a large number of petitions filed to e-People but also to predict the trend of petitions. In detail, we apply text mining techniques to unstructured data of petitions to elicit keywords from petitions and identify groups of petitions with the elicited keywords. Moreover, we apply data mining techniques to structured data of the identified petition groups on purpose to forecast the trend of petitions. Our approach based on applying text and data mining techniques decreases time-consuming manual works on reading and classifying a large number of petitions, and contributes to increasing accuracy in evaluating the trend of petitions. Eventually, it helps petition inspectors to give more attention on detecting and tracking important groups of petitions that possibly grow as nationwide problems. Further, the petitions ordered by their petition groups' trend values can be used as the baseline for making a better decision on the national law and policy strategy.