2006 IEEE International Conference on Granular Computing最新文献

{"title":"A rough set approach to mining concise rules from inconsistent data","authors":"Ying Sai, P. Nie, Ru-zhi Xu, Jincai Huang","doi":"10.1109/GRC.2006.1635808","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635808","url":null,"abstract":"In this paper, a rough set approach to mining concise rules from inconsistent data is proposed. The approach is based on the variable precision rough set model and deals with inconsistent data. By first computing the reduct for each concept, then computing the reduct for each object, this approach adopts a heuristic algorithm HCRI to build concise classification rules for each concept satisfying the given classification accuracy. HASH functions are designed for the implementation, which substantially reduce the computational complexity of the algorithm. UCI data sets are used to test the proposed approach. The results show that our approach effectively eliminates noises in data and greatly improves the total data reduction rate","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133333598","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}

{"title":"Parallel ant colony algorithm for mining classification rules","authors":"Yixin Chen, Ling Chen, Li Tu","doi":"10.1109/GRC.2006.1635763","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635763","url":null,"abstract":"mining classification rules is presented. In the algorithm, each processor is assigned a class label which indicates the consequent parts of the rules it should discover. A group of ants are allocated on each processor to search for the antecedent part of the rules. The ants select the values of the attributes according to the importance of each attribute to the class, the pheromone, and heuristic information. Experimental results on several benchmark datasets show that our algorithm can discover classification rules faster with significantly better accuracy and less redundancy than other methods including the improved Ant-Miner method and the decision-tree-based C4.5 algorithm.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127760283","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}

{"title":"A framework for global constraint checking involving aggregates in multidatabases using granular computing","authors":"P. Madiraju, Rajshekhar Sunderraman, Haibin Wang","doi":"10.1109/GRC.2006.1635851","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635851","url":null,"abstract":"We have earlier introduced constraint checker, a general framework for checking global constraints using an agent based approach. In this paper, we complement the constraint checker with algorithms for checking global constraints involving aggregates in the presence of updates. The algorithms take as input an update statement, a list of global constraints involving aggregates, and granulizes each global constraint into sub constraint granules. The sub constraint granules are executed locally on remote sites and then the algorithm decides if a constraint is violated based on these sub constraint executions. The algorithms are efficient as the global constraint checks are","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128998369","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}

{"title":"Wavesim transform - a new perspective of wavelet transform for temporal data clustering","authors":"R. P. Kumar, P. Nagabhushan","doi":"10.1109/GRC.2006.1635872","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635872","url":null,"abstract":"Wavelets or wavelet analysis or the wavelet transform refers to the representation of a signal in terms of a finite length or fast decaying oscillating waveform known as the mother wavelet. This waveform is scaled and translated to match the input signal. The wavelet transform coefficients has been used as an index of similarity between a function f(t) and the corresponding wavelet, in the fields of pattern recognition and knowledge discovery. In these fields, the coefficients are generated to acquire a set of features. In this paper we explore the possibility of a reverse approach for generating wavelet coefficients by using a conventional similarity measure between the function f(t) and the wavelet. It is a reverse approach from the point that the wavelet coefficients are indices of similarity, and the proposed method is an alternate method to generate a normalized set of similarity indices, whose characteristics are similar to that of wavelet coeffcients. The idea could have lot of impact in future for multiresolution analysis and also can overcome the mathematical complexities induced by wavelet transform. We demonstarte WaveSim transform with an application in temporal data clustering.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128442179","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}

{"title":"A Two-stage Classifier for Protein B-turn Prediction Using Support Vector Machines","authors":"Hua-Sheng Chiu, Hsin-Nan Lin, Allan Lo, Ting-Yi Sung, W. Hsu","doi":"10.1109/GRC.2006.1635907","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635907","url":null,"abstract":"β-turns play an important role in protein structures not only because of their sheer abundance, which is estimated to be approximately 25% of all protein residues, but also because of their significance in high-order structures of proteins. In this study, we introduce a new method of β-turn prediction that uses a two-stage classification scheme and an integrated framework for input features. Ten-fold cross validation based on a benchmark dataset of 426 non-homologue protein chains is used to evaluate our method's performance. The experimental results demon- strate that it achieves substantial improvements over BetaTurn, the current best method. The prediction accuracy, Qtotal, and the Matthews correlation coefficient (MCC) of our approach are 79% and 0.47 respectively, compared to 77% and 0.45 respec- tively for BetaTurn.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115351095","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}

{"title":"An ensemble approach for generating partitional clusters from multiple cluster hierarchies","authors":"Mahmood Hossain, S. Bridges, Yong Wang, J. Hodges","doi":"10.1109/GRC.2006.1635890","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635890","url":null,"abstract":"Traditional clustering is typically based on a single feature set. In some domains, several feature sets may be available to represent the same objects, but it may not be easy to compute an integrated feature set. We have developed the EPaCH (Ensemble method for generating Partitional clusters from multiple Cluster Hierarchies) algorithm to address the problem of combining the results of hierarchical clustering from multiple related datasets where the datasets represent the same set of objects but use different feature sets. EPaCH uses a graph theoretic approach to combine the hierarchies into a single set of partitional clusters. A graph is generated from the hierarchies based on the association strengths of objects in the hierarchies. A graph partitioning algorithm is then applied to generate flat clusters. EPaCH was tested empirically with a document collection consisting of journal abstracts from ten different Library of Congress categories. Both syntactic and semantic feature sets were extracted and the resulting datasets were clus- tered individually using average-link agglomerative hierarchical clustering. EPaCH was then used to generate a single set of flat clusters from the dendrograms. In the document clustering domain, EPaCH is shown to yield higher quality clusters than phylogeny-based ensemble methods and than clustering based on a single feature set for three of four measures of cluster quality.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114783379","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}

{"title":"Discovering e-action rules from incomplete information systems","authors":"Li-Shiang Tsay, Z. Ras","doi":"10.1109/GRC.2006.1635854","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635854","url":null,"abstract":"s Abstract— Action rules, interventions, and E-action rules are examples of knowledge discovery tools for reclassification of objects and for defining actionability as a partially objective concept. However, most of these tools can only deal with incom- pleteness represented as null values. The purpose of knowledge discovery systems is to extract knowledge which is interesting and often interestingness is linked with actionability. In this paper, we present a new algorithm, DEAR 3, to discover actionability knowledge from an incomplete information system. 1 I. INTRODUCTION E-action rules (5), (6), are a key tool used for extracting higher level actionable information from large volumes of data. It can be applied in many real-life applications as a powerful solution to a reclassification problem. The basic principle of reclassification is a process of learning a function that maps a class of objects into another class by changing values of some of the classification attributes describing that class. The classification attributes are divided into stable and flexible. Saying another words, reclassification is a process of showing what changes in values in some of the flexible attributes for a given class of objects are needed in order to shift them from one decision class to another more desired one. E-action rule is a rule of the form ((ω) ∧ (α → β)) ⇒ (φ → ψ), where ω,(α → β), and (φ → ψ) are events. It states that when the fixed condition ω is satisfied and the changeable behavior (α → β) occurs in a database tuples so does the expectation(φ → ψ). Support and confidence are used to measure the importance of each rule to avoid generating irrelevant, spurious, and insignificant rules. Any E-action rule forms workable strategy that can be used in a business decision making process to increase profit, reduce cost, etc. Each E-action rule can be constructed by comparing pairs of previously discovered classification rules from a given decision system. The concept of E-action rule was introduced in (9) to enhance action rules (4) and extended action rules (5),(7), and (8) to extract actionable knowledge from databases containing nominal data. Several efficient algorithms for min- ing E-action rules have been developed (7), (8), (6), and (9). In all these papers, mining for action rules from a complete data is well understood and investigated on both the algorithmic and conceptual level. In this paper, we present a new algorithm DEAR 3f or discovering action rules. It consists of several basic steps: process of discovering classification rules, analyzing them, and process of action rules construction. The development of workable strategies for implementing them is naturally linked with the last step. We also propose a novel method CID for generating classification rules from an incomplete information system. The definition of an incomplete information system that allows to have non-singleton subsets as values of attributes is given. After forming classif","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114890196","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}

{"title":"On designing QoS for congestion control service using neural network predictive techniques","authors":"N. Xiong, Yan Yang, Jing He, Yanxiang He","doi":"10.1109/GRC.2006.1635800","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635800","url":null,"abstract":"With the ever-increasing data transmission appli- cations recently, considerable efforts have been focused on the design of congestion control scheme for data transmission service to guarantee the quality of service (QoS). The main difficulty in designing an efficient congestion controller for data transmission service stems from the heterogeneous receivers, especially those with large propagation delays in data transfer, which also mean the feedbacks arriving at the source are somewhat outdated and can be harmful to the control actions. This usually leads to a mismatch between the network resources and the amount of admitted traffic. To attack this problem, the present paper describes a novel congestion control scheme that is based on a Back Propagation (BP) neural network technique. We consider a general computer communication model with multiple sources and one destination node. This network-assisted property is different from the existed control scheme in that the data source can predict the dynamic of buffer occupancy of the bottleneck node for which the back control packets experience very long propagation delay and probably cause irresponsiveness of a data flow. This active scheme makes the control more responsive to the network status. Thus the rate adaptation can be in a timely manner for the sender to react to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support the data transmission based on feedback of explicit rates' (ER's), and verify this agreement by the simulations. Simulation results show the efficiency of our scheme in terms of quickly response and excellent predictive accuracy.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127170133","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}

{"title":"Rare Event Detection in a Spatiotemporal Environment","authors":"Yusong. Meng, M. Dunham, M. Marchetti, Jie Huang","doi":"10.1109/GRC.2006.1635881","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635881","url":null,"abstract":"In this paper we explore the use of Extensible Markov Models (EMM) to detect rare events in a spatiotemporal environment. This initial work shows that an EMM is scalable, dynamic, and can detect rare events based on spatial values, temporal values, or transitions between real world states. The core of the EMM approach is a combination of clustering and dynamic Markov Chain. Keywords—Extensible Markov Model (EMM), rare event","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126862368","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}

{"title":"Formal automatic verification of security protocols","authors":"M. Xiao, Jinyun Xue","doi":"10.1109/GRC.2006.1635866","DOIUrl":"https://doi.org/10.1109/GRC.2006.1635866","url":null,"abstract":"Security protocols flaws are notoriously difficult to detect. Comparatively little attention has been given to logics of knowledge, although such logics have been proven to be very useful in the specifications of protocols for communication systems. We address ourselves to the analysis of security protocols under the Dolev-Yao model by using a logic of algorithmic knowledge, and propose a general method to describe formally the data structures used in the verification, such as messages, traces, intruders, and so on. We explore the use of our methodology for the verification of security protocols. The Horng-Hsu attack to Helsinki protocol has been found successfully in this setting by using SPIN.","PeriodicalId":400997,"journal":{"name":"2006 IEEE International Conference on Granular Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122011975","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}