Browsing by Author "Hu, YC"

Now showing 1 - 14 of 14

A fuzzy data mining algorithm for finding sequential patterns
(2003-04-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
Shieh, JH
;
科技管理研究所
Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered fist of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
A novel method for discovering fuzzy sequential patterns using the simple fuzzy partition method
(2003-05-01)
Chen, RS
;
Hu, YC
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Sequential patterns refer to the frequently occurring patterns related to time or other sequences, and have been widely applied to solving decision problems. For example, they can help managers determine which items were bought after some items had been bought. However, since fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, they are helpful in building a prototype fuzzy knowledge base in a business. Moreover, each fuzzy sequential pattern consisting of several fuzzy sets described by the natural language is well suited for the thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, since the comprehensibility of fuzzy representation by human users is a criterion in designing a fuzzy system, the simple fuzzy partition method is preferable. In this method, each attribute is partitioned by its various fuzzy sets with pre-specified membership functions. The advantage of the simple fuzzy partition method is that the linguistic interpretation of each fuzzy set is easily, obtained. The main aim of this paper is exactly to propose a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method. Two numerical examples are utilized to demonstrate the usefulness of the proposed method.
Acquisition of compound skills and learning costs for expanding competence sets
(2003-09-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
Chiu, YJ
;
科技管理研究所
For each decision problem, there is a competence set consisting of ideas, knowledge, information, and skills for solving that problem. When decision makers have not acquired the competence set, it is more difficult for them to make decisions. In order to effectively acquire a needed competence set to cope with the problem they face, finding an appropriate learning sequence for acquiring needed single skills for decision makers, the so-called competence set expansion, is very necessary. A compound skill represents a collection of single skills that might be acquired, and some useful compound skills can be added to the needed competence set to help acquire some single skills. To effectively expand the competence set, effective acquisitions of compound skills and learning costs are both necessary. This paper thus proposes a data mining technique to extract potentially useful compound skills from single skills. Subsequently, an effective method is proposed to obtain the learning cost between any two skills. A computer simulation is employed to further show that it is feasible to use those potentially useful compound skills to facilitate the acquisition of single skills through a known integer programming method for expanding the competence set. (C) 2003 Elsevier Ltd. All rights reserved.
Assessing weights of product attributes from fuzzy knowledge in a dynamic environment
(2004-04-01)
Hu, YC
;
Hu, JS
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
Fuzzy knowledge of consumers' frequent purchase behaviors can be extracted from transaction databases. To effectively supporting decision makers, it is necessary to use fuzzy knowledge to assess weights or degrees of consumers' attentiveness to product attributes. From the standpoint of habitual domains, frequent purchase behaviors can be viewed as ideas that are contained in the reachable domain of customers. In addition, this reachable domain is changeable with time, due to the dynamic environment. This paper thus proposes a two-phase learning method with adaptive capability. The first phase builds a fuzzy knowledge base by discovering frequent purchase behaviors from transaction databases; the second phase finds weights of product attributes by a single-layer perceptron neural network. Indeed, customers are asked to evaluate alternatives and attributes through questionnaire. Then, each alternative can be transformed into a piece of input training data for the neural network by the fuzzy knowledge base and part-worths of attributes' levels. After completing the training task, we can find weights from connection weights. Simulation results demonstrate that the proposed methods can use fuzzy knowledge to effectively find customers' attentive degrees of attributes. (C) 2002 Elsevier B.V. All rights reserved.
Competence set expansion for obtaining scheduling plans in intelligent transportation security systems
(2003)
Hu, YC
;
Chiu, YJ
;
Chen, CM
;
Tzeng, GH
;
資訊管理與財務金融系註：原資管所+財金所
In this paper; to reach safe driving, a new method is proposed to acquire project schedules by competence set expansion for developing intelligent transportation systems (ITSS), that can promote security, efficiency and comfort for drivers. Since for each decision problem, there is a competence set consisting of ideas, knowledge, information, and skills needed to solve that problem, this paper treats ITSS as the needed competence set to attain the goal of safe driving. Schedules can be further obtained by using a known method proposed by Li (1999) for expanding competence sets. An empirical study is utilized to demonstrate the usefulness of the proposed method.
Competence sets for deriving two-stage learning sequences from fuzzy knowledge
(2003)
Hu, YC
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
A fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases. Actually, consumers' products preferences and consumers' product buying orders related to purchase behaviors can be found in the fuzzy sequential pattern mining. Since for each decision problem, there is a competence set for solving that problem, we consider knowledge found in fuzzy sequential pattern mining as a needed competence set for solving one decision problem. This paper uses a known competence set expansion method, the minimum spanning table method, to find appropriate two-stage learning sequences that can effectively acquire individual fuzzy knowledge sets found in the fuzzy sequential pattern mining. A numerical example is used to show the usefulness of the proposed method.
Discovering fuzzy association rules using fuzzy partition methods
(2003-04-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
Fuzzy association rules described by the natural language are well suited for the thinking of human subjects and will help to increase the flexibility for supporting users in making decisions or designing the fuzzy systems. In this paper, a new algorithm named fuzzy grids based rules mining algorithm (FGBRMA) is proposed to generate fuzzy association rules from a relational database. The proposed algorithm consists of two phases: one to generate the large fuzzy grids, and the other to generate the fuzzy association rules. A numerical example is presented to illustrate a detailed process for finding the fuzzy association rules from a specified database, demonstrating the effectiveness of the proposed algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.
Discovery of fuzzy sequential patterns for fuzzy partitions in quantitative attributes
(2001)
Chen, RS
;
Tzeng, GH
;
Chen, CC
;
Hu, YC
;
資訊管理與財務金融系註：原資管所+財金所
In this paper we propose the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) to generate all fuzzy sequential patterns from relational database. In FGBSPMA, each quantitative attribute is viewed as a linguistic variable, and can be divided into many candidate 1-dim fuzzy grids. FGBSPMA is consisted of two phases: one is to generate all the large 1-fuzzy sequences, the other is to generate all the fuzzy sequential patterns. FGBSPMA is a efficiently fuzzy sequential patterns mining algorithm, because FGBSPMA scans database only once and applies proper operations on rows of tables to generate large fuzzy sequences and fuzzy sequential patterns. An example is given to illustrate a detailed process for mining the fuzzy sequential patterns from a specified relation. From this example, we can show efficiency and usefulness of FGBSPMA.
Elicitation of classification rules by fuzzy data mining
(2003-10-01)
Hu, YC
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
Data mining techniques can be used to find potentially useful patterns from data and to ease the knowledge acquisition bottleneck in building prototype rule-based systems. Based on the partition methods presented in simple-fuzzy-partition-based method (SFPBM) proposed by Hu et al. (Comput. Ind. Eng. 43(4) (2002) 735), the aim of this paper is to propose a new fuzzy data mining technique consisting of two phases to find fuzzy if-then rules for classification problems: one to find frequent fuzzy grids by using a pre-specified simple fuzzy partition method to divide each quantitative attribute, and the other to generate fuzzy classification rules from frequent fuzzy grids. To improve the classification performance of the proposed method, we specially incorporate adaptive rules proposed by Nozaki et al. (IEEE Trans. Fuzzy Syst. 4(3) (1996) 238) into our methods to adjust the confidence of each classification rule. For classification generalization ability, the simulation results from the iris data demonstrate that the proposed method may effectively derive fuzzy classification rules from training samples. (C) 2003 Elsevier Ltd. All rights reserved.
Finding fuzzy classification rules using data mining techniques
(2003-01-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Significantly, since it is difficult for users to specify the minimum fuzzy support used to determine the frequent fuzzy grids or the minimum fuzzy confidence used to determine the effective classification rules derived from frequent fuzzy grids, therefore the genetic algorithms are incorporated into the proposed method to determine those two thresholds with binary chromosomes. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that the proposed method performs well in comparison with other classification methods. (C) 2002 Elsevier Science B.V. All rights reserved.
Generating learning sequences for decision makers through data mining and competence set expansion
(2002-10-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
For each decision problem, there is a competence set, proposed by Yu, consisting of ideas, knowledge, information, and skills required for solving the problem. Thus, it is reasonable that we view a set of useful patterns discovered from a relational database by data mining techniques as a needed competence set for solving one problem. Significantly, when decision makers have not acquired the competence set, they may lack confidence in making decisions. In order to effectively acquire a needed competence set to cope with the corresponding problem, it is necessary to find appropriate learning sequences of acquiring those useful patterns, the so-called competence set expansion. This paper thus proposes an effective method consisting of two phases to generate learning sequences. The first phase finds a competence set consisting of useful patterns by using a proposed data mining technique. The other phase expands that competence set with minimum learning cost by the minimum spanning table method proposed by Feng and Yu. From a numerical example, we can see that it is possible to help decision makers to solve the decision problems by use of the data mining technique and the competence set expansion, enabling them to make better decisions.
Grey self-organizing feature maps
(2002-10-01)
Hu, YC
;
Chen, RS
;
Hsu, YT
;
Tzeng, GH
;
科技管理研究所
In each training iteration of the self-organizing feature maps (SOFM), the adjustable output nodes can be determined by the neighborhood size of the winning node. However, it seems that the SOFM ignores some important information, which is the relationships that actually exist between the input training data and each adjustable output node, in the learning rule. By viewing input data and each adjustable node as a reference sequence and a comparative sequence, respectively, the grey relations between these sequences can be seen. This paper thus incorporates the grey relational coefficient into the learning rule of the SOFM, and a grey clustering method, namely the GSOFM, is proposed. From the simulation results, we can see that the best result of the proposed method applied for analysis of the iris data outperforms those of other known unsupervised neural network models. Furthermore, the proposed method can effectively solve the traveling salesman problem. (C) 2002 Elsevier Science B.V. All rights reserved.
Low-power branch prediction
(2005)
Hu, YC
;
Chiao, WH
;
Shann, JJJ
;
Chung, CP
;
Chen, WF
Low-power design has gained much attention recently, especially for computing on battery-powered equipments. Reducing BTB (branch target buffer) accesses is an effective way to reduce processor power consumption, since BTB consumes a significant portion of power in a processor. In this paper, we propose two approaches to reduce BTB accesses. The first approach expects the distance of every two dynamic branch instructions to be a constant n, where n can be statically profiled, and forces BTB to repose for n instructions after a BTB hit. The second approach dynamically predicts the address of the next branch instruction, and accesses BTB only on the predicted address. Multimedia/DSP benchmarks are used in our evaluation. Experimental results show that these methods can potentially reduce 22-033% of all BTB accesses.
Mining fuzzy association rules for classification problems
(2002-09-01)
Hu, YC
;
Chen, RS
;
Tzeng, GH
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems. (C) 2002 Elsevier Science Ltd. All rights reserved.