Search

Your search keyword '"Lin, Jerry Chun-Wei"' showing total 5 results
5 results on '"Lin, Jerry Chun-Wei"'

1. Mining skyline frequent-utility patterns from big data environment based on MapReduce framework.

Author

Wu, Jimmy Ming-Tai, Li, Ranran, Wu, Mu-En, and Lin, Jerry Chun-Wei

Subjects
BIG data, DATABASES, WIRELESS Internet, PARALLEL algorithms, DATA mining, INTERNET of things
Abstract

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

2. A Queue Management System for Cloud Data Processing

Author

Shehloo, Arif Ahmad, Butt, Muheet Ahmed, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Lin, Jerry Chun-Wei, editor, Wee, Lai Khin, editor, Bhateja, Vikrant, editor, and Rajesh, T. M., editor

Published
2023
Full Text
View/download PDF

3. Predictive Big Data Analytics Using Multiple Linear Regression Model

Author

Khine, Kyi Lai Lai, Nyunt, Thi Thi Soe, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Zin, Thi Thi, editor, and Lin, Jerry Chun-Wei, editor

Published
2019
Full Text
View/download PDF

5. Fuzzy high-utility pattern mining in parallel and distributed Hadoop framework.

Author

Wu, Jimmy Ming-Tai, Srivastava, Gautam, Wei, Min, Yun, Unil, and Lin, Jerry Chun-Wei

Subjects
*SEQUENTIAL pattern mining, *BIG data
Abstract

• An efficient EFUPM to discover the fuzzy high-utility patterns is proposed. • A Hadoop-based HFUPM is proposed to handle large-scale databases. • Two upper-bounds are then designed to early remove the unpromising candidates. • Experiments showed that the better performance can be obtained. Over the past decade, high-utility itemset mining (HUIM) has received widespread attention that can emphasize more critical information than was previously possible using frequent itemset mining (FIM). Unfortunately, HUIM is very similar to FIM since the methodology determines itemsets using a binary model based on a pre-defined minimum utility threshold. Additionally, most previous works only focused on single, small datasets in HUIM, which is not realistic to any real-world scenarios today containing big data environments. In this work, the fuzzy-set theory and a MapReduce framework are both utilized to design a novel high fuzzy utility pattern mining algorithm to resolve the above issues. Fuzzy-set theory is first involved and a new algorithm called efficient high fuzzy utility itemset mining (EFUPM) is designed to discover high fuzzy utility patterns from a single machine. Two upper-bounds are then estimated to allow early pruning of unpromising candidates in the search space. To handle the large-scale of big datasets, a Hadoop-based high fuzzy utility pattern mining (HFUPM) algorithm is then developed to discover high fuzzy utility patterns based on the Hadoop framework. Experimental results clearly show that the proposed algorithms perform strongly to mine the required high fuzzy utility patterns whether in a single machine or a large-scale environment compared to the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results