Journal ArticleDOI
A novel Cp-Tree-based co-located classifier for big data analysis
TLDR
The aim of this paper is proposing novel co-located classifier to handle complex spatial landslide big data which utilises Cp-Tree algorithm for co- located rule generation to analyse landslide data.Abstract:
The processing capacity, architecture and algorithms of traditional database system are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this paper is proposing novel co-located classifier to handle complex spatial landslide big data. Co-located classification primarily aims at predicting the class labels of the unknown data from the class co-located rules. The main focus is on building a co-located classifier which utilises Cp-Tree algorithm for co-located rule generation to analyse landslide data. The performance of proposed classifier is validated and compared with various data mining classifier.read more
Citations
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Journal ArticleDOI
Big data and disaster management: a systematic review and agenda for future research
TL;DR: This study examines big data in DM to present main contributions, gaps, challenges and future research agenda, and shows a classification of publications, an analysis of the trends and the impact of published research in the DM context.
Book ChapterDOI
A Novel Map-Reduce Based Augmented Clustering Algorithm for Big Text Datasets
K. V. Kanimozhi,M. Venkatesan +1 more
TL;DR: A high scalable speedy and efficient map reduce based augmented clustering algorithm based on bivariate n-gram frequent item to reduce high dimensionality and derive high quality clusters for Big Text documents is presented.
Journal ArticleDOI
Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants
TL;DR: This study addresses the problem of how future students of Software Engineering Capstone Courses can best benefit from their learnings through the use of Artificial Intelligence Virtual Assistant combined with a recommender system.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Proceedings Article
Fast algorithms for mining association rules
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Journal ArticleDOI
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Journal Article
Data Mining Concepts and Techniques
TL;DR: Data mining is the search for new, valuable, and nontrivial information in large volumes of data, a cooperative effort of humans and computers that is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining which produces new, nontrivials information based on the available data set.