YuLung Hsieh - Academia.edu (original) (raw)

Papers by YuLung Hsieh

Research paper thumbnail of Effective application of improved profit-mining algorithm for the interday trading model

TheScientificWorldJournal, 2014

Many real world applications of association rule mining from large databases help users make bett... more Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for v...

Research paper thumbnail of An Effective Mining Algorithm for Profit Mining

2012 International Symposium on Computer, Consumer and Control, 2012

ABSTRACT Association rule mining can be used to discover interesting rules from large databases e... more ABSTRACT Association rule mining can be used to discover interesting rules from large databases easily for better decision making in most real world applications except the financial market. This is because the investors are interested in high profit and low risk trading results more than in those of high confidence and high support. Based on a working model of profit mining, we propose an effective algorithm for investors to find trading rules that include information on profit, risk, and win rate. This mining approach works well not only in the stock market, but also in the futures and other markets.

Research paper thumbnail of ADMiner: An Incremental Data Mining Approach Using a Compressed FP-tree

Journal of Software, 2013

In real world applications, most transaction databases are often large and constantly updated. Cu... more In real world applications, most transaction databases are often large and constantly updated. Current data mining algorithms face the problem of processing a large number of transactions in dynamic environments. Since memory space is limited, it is critical to be able to use available storage efficiently and to process more transactions. In this paper, we propose an improved data structure of a compressed FP-tree to mine frequent itemsets with greater efficiency. Use of our method can minimize the I/O overhead, and, more importantly, it can also perform incremental mining without rescanning the original database. Our experimental results show that the method we propose not only requires less memory, but also performs incremental mining more efficiently.

Research paper thumbnail of Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals

Applied Mathematics & Information Sciences, 2014

In traditional association rule mining algorithms, if the minimum support is set too high, many v... more In traditional association rule mining algorithms, if the minimum support is set too high, many valuable rules will be lost. However, if the value is set too low, then numerous trivial rules will be generated. To overcome the difficulty of setting minimum support values, global and local patterns are mined herein. Owing to the temporal factor in association rule mining, an itemset may not occur frequently in the entire dataset (meaning that it is not a global pattern), but it may appear frequently over specific intervals (meaning that it is a local pattern). This paper proposed a temporal association rule mining algorithm for interval frequent-patterns, called GLFMiner, which automatically and efficiently generates all intervals without prior domain knowledge in an efficient manner. GLFMiner considers not only global frequent-patterns, but also local frequent-patterns. Using the same value of minimum support, it can locate many valuable temporal rules without losing the rules that traditional algorithms may find. Experimental results reveal that our novel algorithm mines more temporal frequent-patterns than traditional association rule mining algorithms and is effective in real-world applications such as market basket analysis and intrusion detection systems.

Research paper thumbnail of Effective application of improved profit-mining algorithm for the interday trading model

TheScientificWorldJournal, 2014

Many real world applications of association rule mining from large databases help users make bett... more Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for v...

Research paper thumbnail of An Effective Mining Algorithm for Profit Mining

2012 International Symposium on Computer, Consumer and Control, 2012

ABSTRACT Association rule mining can be used to discover interesting rules from large databases e... more ABSTRACT Association rule mining can be used to discover interesting rules from large databases easily for better decision making in most real world applications except the financial market. This is because the investors are interested in high profit and low risk trading results more than in those of high confidence and high support. Based on a working model of profit mining, we propose an effective algorithm for investors to find trading rules that include information on profit, risk, and win rate. This mining approach works well not only in the stock market, but also in the futures and other markets.

Research paper thumbnail of ADMiner: An Incremental Data Mining Approach Using a Compressed FP-tree

Journal of Software, 2013

In real world applications, most transaction databases are often large and constantly updated. Cu... more In real world applications, most transaction databases are often large and constantly updated. Current data mining algorithms face the problem of processing a large number of transactions in dynamic environments. Since memory space is limited, it is critical to be able to use available storage efficiently and to process more transactions. In this paper, we propose an improved data structure of a compressed FP-tree to mine frequent itemsets with greater efficiency. Use of our method can minimize the I/O overhead, and, more importantly, it can also perform incremental mining without rescanning the original database. Our experimental results show that the method we propose not only requires less memory, but also performs incremental mining more efficiently.

Research paper thumbnail of Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals

Applied Mathematics & Information Sciences, 2014

In traditional association rule mining algorithms, if the minimum support is set too high, many v... more In traditional association rule mining algorithms, if the minimum support is set too high, many valuable rules will be lost. However, if the value is set too low, then numerous trivial rules will be generated. To overcome the difficulty of setting minimum support values, global and local patterns are mined herein. Owing to the temporal factor in association rule mining, an itemset may not occur frequently in the entire dataset (meaning that it is not a global pattern), but it may appear frequently over specific intervals (meaning that it is a local pattern). This paper proposed a temporal association rule mining algorithm for interval frequent-patterns, called GLFMiner, which automatically and efficiently generates all intervals without prior domain knowledge in an efficient manner. GLFMiner considers not only global frequent-patterns, but also local frequent-patterns. Using the same value of minimum support, it can locate many valuable temporal rules without losing the rules that traditional algorithms may find. Experimental results reveal that our novel algorithm mines more temporal frequent-patterns than traditional association rule mining algorithms and is effective in real-world applications such as market basket analysis and intrusion detection systems.