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Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)最新文献

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A Rule Extraction Method Based on Meta-information 基于元信息的规则抽取方法
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-12-18 DOI: 10.1109/FSKD.2007.116
Jian Su, Wenyong Weng
{"title":"A Rule Extraction Method Based on Meta-information","authors":"Jian Su, Wenyong Weng","doi":"10.1109/FSKD.2007.116","DOIUrl":"https://doi.org/10.1109/FSKD.2007.116","url":null,"abstract":"Rule extraction is an important research area of rough set theory. Many rule extraction methods, such as LEM2, are proposed. However, almost all these methods are on the assumption that they are dealing with a centralized dataset. A costly work of data integration is inevitable for these methods in case of distributed data environment. Meanwhile, meta-information is a compact description of information system or its sub-systems, and the cost of meta-information integration is much less than data integration. Moreover, since the volume of meta-information is much lower than the corresponding original dataset, the cost of operations on the meta-information is comparatively less. In order to take advantage of the meta-information mechanism, a minimal rule set extraction method is proposed in this paper on the basis of meta-information and the complexity of this method is much less than LEM2.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"325 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Two-Way Hybrid Algorithm for Maximal Frequent Itemsets Mining 最大频繁项集挖掘的双向混合算法
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.130
Fu-zan Chen, Min-qiang Li
{"title":"A Two-Way Hybrid Algorithm for Maximal Frequent Itemsets Mining","authors":"Fu-zan Chen, Min-qiang Li","doi":"10.1109/FSKD.2007.130","DOIUrl":"https://doi.org/10.1109/FSKD.2007.130","url":null,"abstract":"A new two-way-hybrid algorithm for mining maximal frequent itemsets is proposed. A flexible two-way-hybrid search method is given. The two-way-hybrid search begins the mining procedure in both the top-down and bottom-up directions at the same time. Moreover, information gathered in the bottom-up can be used to prune the search space in the other top-down direction. Some efficient decomposition and pruning strategies are implied in this method, which can reduce the original search space rapidly in the iterations. Experimental and analytical results are presented in the end.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Structure Learning of Bayesian Networks Based on Vertical Segmentation Data 基于垂直分割数据的贝叶斯网络结构学习
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.533
Hao Huang, Jianqing Huang
{"title":"Structure Learning of Bayesian Networks Based on Vertical Segmentation Data","authors":"Hao Huang, Jianqing Huang","doi":"10.1109/FSKD.2007.533","DOIUrl":"https://doi.org/10.1109/FSKD.2007.533","url":null,"abstract":"A distributed approach in learning a Bayesian networks from vertical segmentation data was promoted in the paper. The approach includes four sequential steps: local learning, sample selection, cross learning, and combination of the results. The main improvement of the algorithm brings forward in the second step. The complex sub-structure of local BN is considered that exist a hidden node which contacts with the sub-structure. The hidden node exist in the other local BN. The experiment proved that the distributed learning method can learn almost the same structure as the result obtained by a centralized learning method.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"34 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124411166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Concept Similarity Based Text Classification Algorithm 基于概念相似度的文本分类算法
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.11
Jing Peng, Dongqing Yang, Shiwei Tang, Jun Gao, P. Zhang, Yan Fu
{"title":"A Concept Similarity Based Text Classification Algorithm","authors":"Jing Peng, Dongqing Yang, Shiwei Tang, Jun Gao, P. Zhang, Yan Fu","doi":"10.1109/FSKD.2007.11","DOIUrl":"https://doi.org/10.1109/FSKD.2007.11","url":null,"abstract":"Text classification is an important task of data mining. Existing algorithms, which based on vector space models, does not considered concept similarities among words, so the accuracy of traditional text classification cannot guarantee. To solve the problem, this paper proposes a new text classification algorithm in Chinese text processing based on concept similarity. The contributions of the paper include: (1) proposing a new similarity-computing model between words or sentences based on concept similarity; (2) applying the algorithm successfully in the text classification of WEB news; (3). analyzing the similarity computing formulas systematically in theory; (4).proving that the algorithm has much more accurate than traditional k-NN algorithm in text classification problems through extensive experiments.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115752981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Chinese Keyword Extraction Based on Word Platform 基于Word平台的中文关键字提取
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.215
Hui Jiao, Qian Liu, Hui-bo Jia
{"title":"Chinese Keyword Extraction Based on Word Platform","authors":"Hui Jiao, Qian Liu, Hui-bo Jia","doi":"10.1109/FSKD.2007.215","DOIUrl":"https://doi.org/10.1109/FSKD.2007.215","url":null,"abstract":"At present researches on Chinese keyword extraction mainly focus on automatic segmentation which is a pretreatment problem. This paper presents a kind of Chinese encoding method based on word platform, and establishes a new Chinese document format in computer. This method makes word the smallest information unit. Chinese keyword extraction does not rely on segmentation by this new method. Thereby the efficiency and quality could be improved. Statistical analysis is adopted to conduct the experiment of keyword extraction based on word platform, and experimental results are satisfying.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116643731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
International Market Selection for Agricultural Product Using Fuzzy Neural Networks 基于模糊神经网络的农产品国际市场选择
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.369
Meimei Zhang, Chuanli Zhuang, Bo Gao
{"title":"International Market Selection for Agricultural Product Using Fuzzy Neural Networks","authors":"Meimei Zhang, Chuanli Zhuang, Bo Gao","doi":"10.1109/FSKD.2007.369","DOIUrl":"https://doi.org/10.1109/FSKD.2007.369","url":null,"abstract":"International market selection is non-linear, multi-criteria decision-making problems characterized by overwhelm complexity. To solve this problem, we proposed a multigroup classification model based on a hybrid fuzzy neural network (FNN) model. The model is combined by the straightforward fuzzy inference system with the back-propagation neural network. Due to the representative ability of fuzzy inference system and the learning or intelligent ability of neural network, the results of the application of the methodology on real data indicate that the proposed method performed remarkable well in the international market selection.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116786315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
A New Metric for Classification of Multivariate Time Series 多元时间序列分类的一种新度量
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.88
Heshan Guan, Q. Jiang, Zhiling Hong
{"title":"A New Metric for Classification of Multivariate Time Series","authors":"Heshan Guan, Q. Jiang, Zhiling Hong","doi":"10.1109/FSKD.2007.88","DOIUrl":"https://doi.org/10.1109/FSKD.2007.88","url":null,"abstract":"Multivariate time series are an important kind of data collected in many domains, such as multimedia, biology and so on. We focus on discrimination metric for time series data; especially classify the multivariate time series as stationary or non-stationary. In this paper we present a new metric, the nonlinear trend of the cross-correlation matrix, for classification of multivariate time series, which could well depict the stationarity of multivariate time series. The proposed approach has been tested using two datasets, one natural and one synthetic, and is shown to our metric is more efficient than the benchmark metric in all cases. We take K-means clustering in the experiment.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116800301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Text Clustering Algorithm Based on Lexical Graph 基于词汇图的文本聚类算法
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.560
Yun Sha, Guoying Zhang, Huina Jiang
{"title":"Text Clustering Algorithm Based on Lexical Graph","authors":"Yun Sha, Guoying Zhang, Huina Jiang","doi":"10.1109/FSKD.2007.560","DOIUrl":"https://doi.org/10.1109/FSKD.2007.560","url":null,"abstract":"Text clustering methods can group text into thematic clusters, which is an important topic in many fields, such as search engine. The well-known methods of text clustering, however, do not really address the special problems of text clustering because of the very high dimensionality data and understandability of the cluster description. An algorithm for text clustering based on lexical graph is proposed in this paper, which is a kind of term-based cluster method. The lexical graph is build with nodes representing words and edges representing their concurrent in text. The attribute of each node is text which the word occurs in. A cluster center is defined as node (word) with large degree in this graph, the center attributes (text occurs in) and its neighbors' are partitioned to one cluster whose description is the center node. This approach reduces drastically the dimensionality of the data and improves the synonymy extension ability. An experimental evaluation on Web documents as well as classical text documents on demonstrates that the proposed algorithms obtain clustering of comparable quality significantly more efficiently than K-Means and STC algorithms on the search results data set. Furthermore, this method provides an understandable description of the discovered clusters by their center.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117169756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Matching Scenarios Patterns by Using Linear Programming 用线性规划匹配场景模式
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.389
Guoxing Zhao, B. Luo, Jixin Ma
{"title":"Matching Scenarios Patterns by Using Linear Programming","authors":"Guoxing Zhao, B. Luo, Jixin Ma","doi":"10.1109/FSKD.2007.389","DOIUrl":"https://doi.org/10.1109/FSKD.2007.389","url":null,"abstract":"This paper continues the work presented previously at ICNC-FSKD-05 for representing and matching scenario patterns. A unified scheme is presented to replace the two previous equivalent schemas for formalizing scenario patterns. In the unified scheme, a scenario is denoted in terms of a collection of states with the corresponding temporal constraints, where a state is defined as a set of Boolean-valued time-dependent fluents. The concept of a scenario graph is formally introduced as a directed, partially weighted and labeled simple graph. Based on such a graphical representation, an extended linear programming graph matching algorithm is proposed for recognizing scenario patterns.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"39 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120837729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
TDML: A Data Mining Language for Transaction Databases TDML:用于事务数据库的数据挖掘语言
Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) Pub Date : 2007-08-24 DOI: 10.1109/FSKD.2007.558
A. Muthukumar, R. Nadarajan
{"title":"TDML: A Data Mining Language for Transaction Databases","authors":"A. Muthukumar, R. Nadarajan","doi":"10.1109/FSKD.2007.558","DOIUrl":"https://doi.org/10.1109/FSKD.2007.558","url":null,"abstract":"A desired feature of data mining systems is the ability to support ad hoc and interactive data mining in order to facilitate flexible and effective knowledge discovery. Data mining query languages can be designed to support such a feature. There are data mining query languages like DMQLfor mining relational databases. In this paper, we have proposed a new data mining language for mining transaction databases called TDML. This proposed language mines association rule mining and sequential pattern mining. It uses a new bit map processing approach with buffered storage of results. Various types of data mining approaches that are supported like generalized mining, multilevel mining, multidimensional mining, distributed mining, partition mining, incremental mining, online mining, merge mining, transaction reduction, stream mining and targeted itemset mining.","PeriodicalId":201883,"journal":{"name":"Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121274692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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