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Now showing items 97 - 112 of 139

  • Fuzzy palmer Scheduling for Flow Shops with More than Two Machines

    Tzung-Pei Hong   Tzung-Nan Chuang  

    In the past, we have demonstrated how fuzzy concepts can easily be used in the Johnson algorithm to manage uncertain scheduling on two-machine flow shops. This paper extends application to fuzzy flow shops with more than two machines. A new fuzzy heuristic flow-shop scheduling algorithm (the fuzzy Palmer algorithm) is then designed since optimal solutions seem unnecessary for uncertain environments. Also, the conventional Palmer algorithm is presented as a special case of the fuzzy Palmer algorithm with special assigned membership functions.
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  • Mining weighted browsing patterns with linguistic minimum supports

    Tzung-Pei Hong   Ming-Jer Chiang   Shyue-Liang Wang  

    All the web pages are usually assumed to have the same importance in web mining. Different web pages In a web site may, however, have different importance to users in real applications. Besides, the mining parameters In most conventional data-mining algorithms are numerical. This paper thus attempts to propose a weighted web-mining technique to discover linguistic browsing patterns from log data in web servers. Web pages are first evaluated by managers as linguistic terms to reflect their Importance, which are then transformed as fuzzy sets of weights. Linguistic minimum supports are assigned by users, showing a more natural way of human reasoning. Fuzzy operations including fuzzy ranking are then used to find linguistic weighted large sequences. An example is also given to clearly illustrate the proposed approach.
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  • A practical extension of web usage mining with intentional browsing data toward usage

    Yu-Hui Tao   Tzung-Pei Hong   Wen-Yang Lin   Wen-Yuan Chiu  

    Intentional browsing data is a new data component for improving Web usage mining that uses Web log files as the primary data source. Previously, the Web transaction mining algorithm was used in e-commerce applications to demonstrate how it could be enhanced by intentional browsing data on pages with item purchase and complemented by intentional browsing data on pages without item purchase. Although these two intention-based algorithms satisfactorily illustrated the benefits of intentional browsing data on the original Web transaction mining algorithm, three potential issues remain: Why is there a need to separate the source data into purchased-item and not-purchased-item segments to be processed by two intention-based algorithms? Moreover, can the algorithms contain more than one browsing data types? Finally, can the numeric intention-based data counts be more user friendly for decision-making practices? To address these three issues, we propose a unified intention-based Web transaction mining algorithm that can efficiently process the whole data set simultaneously with multiple intentional browsing data types as well as transform the intentional browsing data counts into easily understood linguistic items using the fuzzy set concept. Comparisons and implications for e-commerce are also discussed. Instead of addressing the technical innovation in this extension study, the revised intention-based Web usage mining algorithm should make its applications much easier and more useful in practice. 2008 Elsevier Ltd. All rights reserved.
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  • A Knowledge-Acquisition Strategy Based on Genetic Programming

    Chan-Sheng Kuo   Tzung-Pei Hong   Chuen-Lung Chena  

    In this paper, we have modified our previous GP-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge evolution, and knowledge output. One new genetic operator, separation, is designed in the proposed approach to remove contradiction, thus producing more accurate classification rules. A subtree pruning technique is also used to restrain the classification trees excessively expanding in the evolutionary process. Experimental results from diagnosis of breast cancers also show the feasibility of the proposed algorithm.
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  • Maintaining the Fast Updated FP-tree from Modified Records Based on Pre-large Itemsets

    Wen-Hsiang Lu   Tzung-Pei Hong   Chun-Wei Lin  

    In this paper; maintenance based we modify the FUFP-tree on the concept of pre-large itemsets for efficiently handling record modification. The proposed approach can achieve a good execution time for tree maintenance especially when each time a small number of records are modified. Experimental results show that the proposed pre-FUFP modification algorithm has a good performance for handling updated records and generate nearly the same tree structure as the original FP tree algorithm.
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  • Discovery of Fuzzy Multiple-Level Web Browsing Patterns

    Shyue-Liang Wang   Wei-Shuo Lo   Tzung-Pei Hong  

    Web usage mining is the application of data mining techniques to discover usage patterns from web data. It can be used to better understand web usage and better serve the needs of rapidly growing web-based applications. Discovery of browsing patterns; page clusters; user clusters; association rules and usage statistics are some usage patterns in the web domain. Web mining of browsing patterns including simple sequential patterns and sequential patterns with browsing times has been studied recently. However; most of these works focus on mining browsing patterns of web pages directly. In this work; we introduce the problem of mining browsing patterns on multiple levels of a taxonomy comprised of web pages. The browsing time on each web page is used to analyze the retrieval behavior. Since the data collected are numeric; fuzzy concepts are used to process them and to form linguistic terms. A web usage-mining algorithm to discover multiple-level browsing patterns from linguistic data is thus proposed. Each page uses only the linguistic term with maximum cardinality in later mining processes; thus making the number of fuzzy regions to be processed the same as the number of pages. Computation time can thus be greatly reduced. In addition; the inclusion of concept hierarchy (taxonomy) of web pages produces browsing patterns of different granularity. This allows the views of users’ browsing behavior from various levels of perspectives.
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  • Using divide-and-conquer GA strategy in fuzzy data mining

    Tzung-Pei Hong   Chun-Hao Chen   Yu-Lung Wu   Yeong-Chyi Lee  

    to derive a predefined number of membership functions for getting a maximum profit within an interval of user specified minimum support values. induce association rules from transaction data. In [4]; we proposed a mining approach that Transactions in real-world applications; however; . integrated fuzzy-set concepts with the apriori mining usually consist of quantitative values. This paper thus algorithm [l] to find interesting itemsets and fuzzy proposes a fuzzy data-mining algorithm for fxtracting association rules in transaction data with quantitative both association rules and membership functions from values. In that paper; the membership functions were quantitative transactions. A GA-based framework for assumed to be known in advance. The given finding membership functions suitable for mining membership functions may; however; have a critical problems i s proposed. The fitness of each set of influence on the final mining results. This paper thus membership functions is evaluated using the fuzzymodifies the previous algorithm and proposes a new supports of the linguistic terms in the large I-itemsets fuzzy data-mining algorithm for extracting both and the suitability of the derived membership functions. association rules and membership functions from The proposed framework .thus maintains multiple quantitative transactions. The proposed algorithm can populations of membership functions; with one dynamically adapt membership functions by genetic population for one item's membership functions. The algorithms and uses them to fizzify the quantitative final best set of membership functions gathered from transactions. Our previous fuzzy mining approach [5] all the populations is used to effectively mine fuzzy can thus be easily used to find fuzzy association rules. association rules. Data mining is most commonly used in attempts to
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  • Incremental data mining based on two support thresholds

    Tzung-Pei Hong   Ching-Yao Wang   Yu-Hui Tao  

    Proposes the concept of pre-large item sets and designs a novel, efficient incremental data mining algorithm based on it. Pre-large item sets are defined using two support thresholds (a lower support threshold and an upper support threshold) to reduce re-scanning of the original databases and to save maintenance costs. The proposed algorithm doesn't need to re-scan the original database until a number of transactions have arrived. If the size of the database is growing larger, then the allowed number of new transactions will be larger too. Therefore, along with the growth of the database, our proposed approach is increasingly efficient. This characteristic is especially useful for real applications.
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  • An Efficient Pruning and Filtering Strategy to Mine Partial Periodic Patterns from a Sequence of Event Sets

    Kung-Jiuan Yang   Tzung-Pei Hong   Yuh-Min Chen   Guo-Cheng Lan  

    Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the authors have adopted the projection-based approach to discover the partial periodic patterns from single-event time series. In this paper, the authors extend it to mine partial periodic patterns from a sequence of event sets which multiple events concurrently occur at the same time stamp. Besides, an efficient pruning and filtering strategy is also proposed to speed up the mining process. Finally, the experimental results on a synthetic dataset and real oil price dataset show the good performance of the proposed approach. An Efficient Pruning and Filtering Strategy to Mine Partial Periodic Patterns from a Sequence of Event Sets
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  • A Key Renewal Scheme under the Power Consumption for Wireless Sensor Networks

    Chien-Lung Wang   Tzung-Pei Hong   Gwoboa Horng   Wen-Hung Wang  

    In this paper; we propose a distributed; reliable and low-power-consumed key-renewal scheme for wireless sensor networks. The proposed scheme is divided into parts: the server part and the sensor-node part. The server will produce an appropriate key-generation function (KGF) for key renewal on sensor nodes under a power consumption constraint. It divides the function into slices and sends the slices to sensor nodes. The sensor nodes will assemble the slices to rebuild the key-generation function for key renewal as they receive the slices. The experimental results also show the effectiveness of the proposed key renewal scheme.
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  • An Efficient Algorithm for Mining Sequential Rules with Interestingness Measures

    Thi-Thiet Pham   Jiawei Luo   Tzung-Pei Hong   Bay Vo  

    Mining sequential rules are an important problem in data mining research. It is commonly used for market decisions; management and behaviour analysis. In traditional association-rule mining; rule interestingness measures such as confidence are used for determining relevant knowledge. They can reduce the size of the search space and select useful or interesting rules from the set of the discovered ones. Many studies have examined the interestingness measures for mining association rules; but have not been devoted to mine sequential rules in sequence databases. In this paper; we thus consider and apply several interestingness measures to generate all relevant sequential rules from a sequence database. The prefix tree structure is also used to get the support values of sequential patterns faster and reduce the execution time for mining sequential rules. Our experimental results show that the run time for mining sequential rules with interestingness measures on the prefix tree structure is much faster than that of other algorithms.
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  • A multi-level ant-colony mining algorithm for membership functions

    Tzung-Pei Hong   Ya-Fang Tung   Shyue-Liang Wang   Yu-Lung Wu   Min-Thai Wu  

    Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of binary bits to encode the membership functions. This paper elaborates on the original approach to increase the accuracy of results by adding multi-level processing. A multi-level ant colony framework is thus designed and an algorithm based on the structure is proposed to achieve the purpose. The proposed approach first transforms the fuzzy mining problem into a multi-stage graph, with each route representing a possible set of membership functions. The new approach then extends the previous one, using multi-level processing to solve the problem in which the maximum quantities of item values in the transactions may be large. The membership functions derived in a given level will be refined in the subsequent level. The final membership functions in the last level are then outputted to the rule-mining phase to find fuzzy association rules. Experiments are also performed to show the performance of the proposed approach. The experimental results show that the proposed multi-level ant colony systems mining approach can obtain improved results. 2011 Elsevier Inc. All rights reserved.
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  • Mining fuzzy sequential patterns from quantitative data

    Tzung-Pei Hong   Chan-Sheng Kuo   Sheng-Chai Chi  

    Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the conventional data mining algorithms can identify the relationships among transactions with binary values. Temporal transactions with quantitative values are, however, commonly seen in real-world applications. This paper thus attempts to propose a new data mining algorithm, which takes advantage of fuzzy set theory to enhance the capability of exploring interesting sequential patterns from databases with quantitative values. The proposed algorithm integrates the concepts of fuzzy sets and the AprioriAll algorithm to find interesting sequential patterns and fuzzy association rules from transaction data.
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  • Learning coverage rules from incomplete data based on rough sets

    Tzung-Pei Hong   Li-Huei Tseng   Been-Chian Chien  

    In this paper, we deal with the problem of producing a set of certain and possible rules for coverage of incomplete data sets based on rough sets. All the coverage rules gathered together can cover all the given training examples. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given incomplete training examples. One of the best equivalence classes in incomplete lower or upper approximations is chosen according to some criteria. The objects covered by the incomplete equivalence class are then removed from the incomplete training set. The same procedure is repeated to find the coverage set of rules. The training examples and the approximations then interact on each other to find the maximally general coverage rules and to estimate appropriate unknown values. The rules derived can then be used to build a prototype knowledge base.
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  • An Efficient FUSP-Tree Update Algorithm for Deleted Data in Customer Sequences

    Wen-Hsiang Lu   Tzung-Pei Hong   Chun-Wei Lin  

    In the past; the fast-updated sequential-pattern tree (call FUSP-tree) structure was proposed for mining sequential patterns from a set of customer sequences. An incremental mining algorithm was also designed for handling newly added transactions. Since data may also be deleted in real applications; an FUSP-tree maintenance algorithm for deletion of customer sequences is thus proposed in this paper for reducing the execution time in reconstructing the tree. Experimental results also show that the proposed tree-update algorithm has a good performance than the batch FUSP-tree algorithm for handling the deletion of customer sequences. The proposed tree-update algorithm thus makes the tree update process become easy and efficient.
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  • A maximum channel reuse scheme with Hopfield Neural Network based static cellular radio channel allocation systems

    Tzung-Pei Hong   Chiu-Ching Tuan   Jie-Hung Lee  

    In recent years, wireless and mobile communication systems become increasingly popular. The demand for mobile communication has thus made the industry put more efforts towards designing new-generation systems. One of the important issues in mobile-phone communications is about the static channel assignment problem (SCAP). Although many techniques have been proposed for SCAP, a challenge for the cellular radio communication system is how to enhance and maximize the frequency reuse. The general SCAP is known as an NP-hard problem. The static channel assignment scheme based on the Hopfield Neural Network was shown to perform well when compared to some other schemes such as graph coloring and genetic algorithm (GA). In this paper, we extend Kim et al.’;s modified Hopfield Neural Network methods and focus on channel reusing to obtain a near-optimum solution for CAP. Several constraints are considered for obtaining the desired results. Eight-benchmark problems are simulated and the energy evolution process is discussed. Simulation results demonstrated that the proposed scheme could make higher channel reuse rate.
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