Smart Health
International Conference, ICSH 2013, Bei**g, China, August 3-4, 2013. Proceedings
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27 Result(s)
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Vincent S. Tseng
Computer Science
Database Management
Information Storage and Retrieval
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Chapter and Conference Paper
Mining Trending High Utility Itemsets from Temporal Transaction Databases
In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power ...
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Article
EFIM: a fast and memory efficient algorithm for high-utility itemset mining
In recent years, high-utility itemset mining has emerged as an important data mining task. However, it remains computationally expensive both in terms of runtime and memory consumption. It is thus an important...
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Chapter and Conference Paper
A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset
The use of intelligent technologies in clinical decision making have started playing a vital role in improving the quality of patients’ life and hel** in reduce cost and workload involved in their daily heal...
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Chapter and Conference Paper
Mining High-Utility Itemsets with Both Positive and Negative Unit Profits from Uncertain Databases
Some important limitation of frequent itemset mining are that it assumes that each item cannot appear more than once in each transaction, and all items have the same importance (weight, cost, risk, unit profit...
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Chapter and Conference Paper
IRS-HD: An Intelligent Personalized Recommender System for Heart Disease Patients in a Tele-Health Environment
The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and hel** to reduce cost and workload involved in th...
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Chapter and Conference Paper
Mining Minimal High-Utility Itemsets
Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset...
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Chapter and Conference Paper
Efficient Mining of Uncertain Data for High-Utility Itemsets
High-utility itemset mining (HUIM) is emerging as an important research topic in data mining. Most algorithms for HUIM can only handle precise data, however, uncertainty that are embedded in big data which col...
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Chapter and Conference Paper
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning
High utility itemset mining is a challenging task in frequent pattern mining, which has wide applications. The state-of-the-art algorithm is HUI-Miner. It adopts a vertical representation and performs a depth-...
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Chapter and Conference Paper
ERMiner: Sequential Rule Mining Using Equivalence Classes
Sequential rule mining is an important data mining task with wide applications. The current state-of-the-art algorithm (RuleGrowth) for this task relies on a pattern-growth approach to discover sequential rule...
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Chapter and Conference Paper
WBPL: An Open-Source Library for Predicting Web Surfing Behaviors
We present WBPL (Web users Behavior Prediction Library), a cross-platform open-source library for predicting the behavior of web users. WBPL allows training prediction models from server logs. The proposed lib...
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Article
An efficient projection-based indexing approach for mining high utility itemsets
Recently, utility mining has widely been discussed in the field of data mining. It finds high utility itemsets by considering both profits and quantities of items in transactional data sets. However, most of t...
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Chapter and Conference Paper
Novel Concise Representations of High Utility Itemsets Using Generator Patterns
Mining High Utility Itemsets (HUIs) is an important task with many applications. However, the set of HUIs can be very large, which makes HUI mining algorithms suffer from long execution times and huge memory cons...
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Article
Efficient algorithms for discovering high utility user behavior patterns in mobile commerce environments
Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential pur...
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Article
A hybrid scheme for energy-efficient object tracking in sensor networks
Energy saving is a critical issue in many sensor-network-based applications. Among the existing sensor-network-based applications, the surveillance application has attracted extensive attention. Object trackin...
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Book and Conference Proceedings
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Article
Preference-oriented mining techniques for location-based store search
With the development of wireless telecommunication technologies, a number of studies have been done on the issues of location-based services due to wide applications. Among them, one of the active topics is th...
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Chapter and Conference Paper
Using Partially-Ordered Sequential Rules to Generate More Accurate Sequence Prediction
Predicting the next element(s) of a sequence is a research problem with wide applications such as stock market prediction, consumer product recommendation, and web link recommendation. To address this problem,...
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Chapter and Conference Paper
A One-Phase Method for Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments
Mobile sequential pattern mining is an emerging topic in data mining fields with wide applications, such as planning mobile commerce environments and managing online shop** websites. However, an important fa...
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Chapter and Conference Paper
Discovering Valuable User Behavior Patterns in Mobile Commerce Environments
Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential pur...
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Chapter and Conference Paper
Mining Top-K Non-redundant Association Rules
Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or gener...
