26 Result(s)
within
Computer Science
Database Management
Vincent S. Tseng
Conference Paper
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Chapter and Conference Paper
Correction to: Database Systems for Advanced Applications
The original version of the book was inadvertently published with incorrect acknowledgements in chapters 28 and 31. The acknowledgements have been corrected and read as follows: Acknowledgement: “This paper is...
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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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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
EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To p...
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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
Efficient Mining of High-Utility Sequential Rules
High-utility pattern mining is an important data mining task having wide applications. It consists of discovering patterns generating a high profit in databases. Recently, the task of high-utility sequential p...
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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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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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Chapter and Conference Paper
TripCloud: An Intelligent Cloud-Based Trip Recommendation System
With the advance of Location-Based Services (LBS), researches on trip recommendation have attracted extensive attentions. Among them, one active topic is trip planning. In the previous studies on trip planning...
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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...
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Chapter and Conference Paper
Effective Content-Based Music Retrieval with Pattern-Based Relevance Feedback
To retrieve the preferred music piece from a music database, contentbased music retrieval has been studied for several years. However, it is not easy to retrieve the desired music pieces within only one query ...
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Chapter and Conference Paper
Mining Top-K Sequential Rules
Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become v...
