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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Personal rules generation for intelligent e-mail service system
(2000-06-01)
Tsai, CJ
;
Tseng, SS
;
Cheng, HT
;
資訊工程學系
;
Department of Computer Science
As E-mail becomes more popular over network, the problem of receiving a lot of undesired E-mails arise. All of the previous systems, which have been developed to solve this problem, may be divided into two different categories, client-side, and server-side, from the view of location of filter. The former case always causes waste of network traffic and the latter case results that ail users may only share a small predefined set of filtering rules. In this paper, we propose a new architecture of E-mail service system, called Intelligent E-mail Service System (IESS), integrating client-side and server-side to help users manage their E-mails. A data mining approach is used in client to find managing rules from user's behavior of reading E-mail automatically. Then these rules would be stored in rule base for predicating E-mail at server. The prediction of the E-mail is made before transferring it to client; therefore, the undesired E-mails can be filtered at server to save network traffic. In addition, the feedback mechanism is provided to refine the prediction accuracy based on each user's decisions for reading E-mails. Experimental results show that IESS can infer E-mail reading preference of individual users correctly up to the accuracy about 80%. We may conclude that IESS can help users manage their E-mails effectively.
Data types generalization and transformation for data mining algorithms
(2000-06-01)
Jiang, MF
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Tseng, SS
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Liao, SY
;
資訊工程學系
;
Department of Computer Science
In most applications, knowing the characteristics of the data mining methods is usually the key factor to determine the kind of data mining methods should be applied. In this work, we first survey and analyze six kinds of data mining algorithms and four kinds of data forms, and then find out the relations between the mining algorithms and the data types. According to the relations, the suitable generalized data types will be created and is then used to transform the generalized data types of data sources to the suitable one for the selected mining algorithm. With the proposed preprocessing work for data mining algorithms, users can select appropriate mining algorithm just for the goal of application without considering the data types. Finally. two applications for e-mail management and e-mail log analysis are proposed to show the practicality of our method.
A two-phase fuzzy mining and learning algorithm for adaptive learning environment
(2001)
Tsai, CJ
;
Tseng, SS
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Lin, CY
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資訊工程學系
;
Department of Computer Science
As computer-assisted instruction environment becomes more popular over the world, the analysis of historical learning records of students becomes more important. In this work, we propose a Two-Phase Fuzzy Mining and Learning Algorithm, integrating data mining algorithm, fuzzy set theory, and look ahead mechanism, to find the embedded information, which can be provided to teachers for further analyzing, refining or reorganizing the teaching materials and tests, from historical learning records.
Detecting intrusion patterns using bit-wise indexing method
(2001)
Chen, WC
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Tseng, SS
;
Ou, CH
;
資訊工程學系
;
Department of Computer Science
Although there are many available computer security methods (like Identification and authentication (I&A), Access Control and Firewalls), the computer security is still a big problem today due to the network protocol and operating system flaws and the variances of intrusions. Building an intrusion detection system has become one of the most important topics in the computer security domain. In order to identify the intrusion behavior, the expression of intrusion patterns becomes a very important issue for a success intrusion detection system. In this paper, we propose a new, efficient and service-oriented intrusion pattern mining and representation method, called Bit-wise-based Intrusion Pattern Mining Method (BIPAM), which can provide higher performance, better maintenance and expressive abilities. These bit-wised-based patterns can be the description of intrusion behaviors of the online intrusion detection systems and thus enhance the efficiency and extendibility of such systems.
Protocol based foresight anomaly intrusion detection system
(2003)
Tsai, MK
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Lin, SC
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Tseng, SS
;
資訊工程學系
;
Department of Computer Science
In recent years, a lot of network intrusion detection systems (NIDSs) have been developed to assist administrators in detecting the malevolent attacks. However, most of the NIDSs may not prevent attacks in advance. In order to prevent attacks, a Finite Automata Based Foresight Network Intrusion Detection System (FA-FNIDS) will be proposed in this paper. The FA-FNIDS can prevent attacks and further find novel attacks. Finally, three experiments will also be done for evaluating the efficiency of our FA-FNIDS.
A data mining project for solving low-yield situations of semiconductor manufacturing
(2004)
Chen, WC
;
Tseng, SS
;
Hsiao, KR
;
Liu, CC
;
資訊工程學系
With huge amount of semiconductor engineering data stored in database and versatile analytical charting and reporting in production and development, the CIM/MES/EDA systems in the most semiconductor manufacturing companies help users to analyze the collected data in order to achieve the goal of yield enhancement. However, the procedures of semiconductor manufacturing are sophisticated and the collected data among these procedures are thus becoming high-dimensional and huge. Currently, some statistical methods, such like K-W test, covariance analysis, regression analysis, etc., have been used to analyze the information summarized from EDA system, and thus generate too many indexes that can not be easily judged and assimilated by engineers. Besides, too many false alarms may be raised and lots of time is required to check the factuality among them. In order to deal with the large amount and high-dimensional data, the data mining technologies are thus used to solve such problems. In this paper, we would like to propose a data mining solution and describe the experiences applying such solutions for discovering the root causes of low-yield situations in a worldwide semiconductor manufacturing company. Also, the situation of applying such mining solution for manufacturing defects detection in semiconductor manufacturing domain will be reviewed Finally, the architecture of a reasonable, reliable and flexible data mining system will be briefly described.
A new approach for constructing the concept map
(2004)
Sue, PC
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Weng, JF
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Su, JM
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Tseng, SS
;
資訊工程學系
For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners' historical testing records. Phase I is used to preprocess the testing records. We apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic, apply Education Theory (Item Analysis for Norm-Referencing) to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also evaluated.
Multidimensional on-line mining
(2006)
Wang, CY
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Hong, TP
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Tseng, SS
;
資訊工程學系
;
Department of Computer Science
In the past;, incremental mining approaches usually considered getting the newest; set; of knowledge consistent; with the entire set of data inserted so far. Users can not, however, use them to obtain rules or patterns only from their interesting portion of the data. In addition, these approaches only focused on finding frequent patterns in a specified part of a database. That is, although the data records are collected in under certain time, place and category, such contexts (circumstances) have been ignored in conventional mining algorithms. It will cause the lack of patterns or rules to help users solve problems at, different, aspects and with diverse considerations. In this paper, we thus attempt; to extend incremental mining to online decision support under multidimensional context considerations. We first propose the multidimensional pattern relation to structurally and systematically retain the additional context information and mining information for each inserted dataset into a database. We then develop an algorithm based on the proposed multidimensional pattern relation to correctly and efficiently fulfill diverse on-fine mining requests.
Learning portfolio analysis and mining for SCORM compliant environment
(2006)
Su, JM
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Tseng, SS
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Wang, W
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Weng, JF
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Yang, JTD
With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM) 2004 provides the Sequencing and Navigation (SN) Specification to define the course sequencing behavior, control the sequencing, selecting and delivering of course, and organize the content into a hierarchical structure, namely Activity Tree. Therefore, how to provide customized course according to individual learning characteristics and capabilities, and how to create, represent and maintain the activity tree with appropriate associated sequencing definition for different learners become two important issues. However, it is almost impossible to design personalized learning activities trees for each learner manually. The information of learning behavior, called learning portfolio, can help teacher understand the reason why a learner got high or low grade. Thus, in this paper, we propose a Learning Portfolio Mining (LPM) Approach including four phases: 1. User Model Definition Phase: define the learner profile based upon existing articles and pedagogical theory. 2. Learning Pattern Extraction Phase: apply sequential pattern mining technique to extract the maximal frequent learning patterns from the learning sequence, transform original learning sequence into a bit vector, and then use distance based clustering approach to group learners with good learning performance into several clusters. 3. Decision Tree Construction Phase: use two third of the learner profiles with corresponding cluster labels as training data to create a decision tree, and the remainings are the testing data. 4. Activity Tree Generation Phase: use each created cluster including several learning patterns as sequencing rules to generate personalized activity tree with associated sequencing rules of SN. Finally, for evaluating our proposed approach of learning portfolio analysis, an experiment has been done and the results show that generated personalized activity trees with sequencing rules are workable and beneficial for learners.
A performance-based approach to dynamic workload distribution for master-slave applications on grid environments
(2006)
Shih, WC
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Yang, CT
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Tseng, SS
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資訊工程學系
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Department of Computer Science
Effective workload distribution techniques can significantly reduce the total completion time of a program on and computing environments. In this paper, we propose a dynamic performance-based workload partition approach for master-slave types of applications on grids. Furthermore, we implement two types of applications and conduct the experimentations on our grid testbed. Experimental results showed that our method could execute more efficiently than traditional schemes.

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