Given publication titles and authors, what can we say about the evolution of scientific topics and communities over time? Which communities shrunk, which emerged, and which split, over time? And, when in time were the...
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ISBN:
(纸本)9781424418367
Given publication titles and authors, what can we say about the evolution of scientific topics and communities over time? Which communities shrunk, which emerged, and which split, over time? And, when in time were the turning points? We propose TimeFall, which can automatically answer these questions given a social network/graph that evolves over time. The main novelty of the proposed approach is that it needs no user-defined parameters, relying instead on the principle of Mnimum Description Length (MDL), to extract the communities, and to find good cut-points in time when communities change abruptly: a cut-point is good, if it leads to shorter data description. We illustrate our algorithm on synthetic and large real datasets, and we show that the results of the TimeFall agree with human intuition.
Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acqu...
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Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and datamining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and datamining, and Knowledge Discovery in database (KDD) process are presented in the paper. datamining theory, datamining tasks, datamining technology and datamining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.
We present a number of computational complexity results for an Optical model of computation called the continuous space machine. We also describe an implementation for an optical computing algorithm that, can be easil...
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ISBN:
(纸本)9783540856726
We present a number of computational complexity results for an Optical model of computation called the continuous space machine. We also describe an implementation for an optical computing algorithm that, can be easily defined within the model. Our optical model is designed to model a wide class of optical computers, such as matrix vector multipliers and patternrecognition architectures. It is known that the model solves intractable PSPACE problems in polynomial time, and NC problems in polylogarithmic time. Both of these results use large spatial resolution (number of pixels). Here we look at what happens when we have constant spatial resolution. It turns out that we obtain similar results by exploiting other resources, such as dynamic range and amplitude resolution. However, with certain other restrictions we essentially have a sequential device. Thus we are exploring the border between parallel and sequential computation in optical computing. We describe an optical architecture for the unordered search problem of finding a one in a list of zeros. We argue that our algorithm scales well, and is relatively straightforward to implement. This problem is easily parallelisable and is from the class NC. We go on to argue that the optical computing community should focus their attention on problems within P (and especially NC), rather than developing systems for tackling intractable problems.
Model selection is an important problem in statistics, machinelearning, and datamining. In this paper, we investigate the problem of enabling multiple parties to perform model selection on their distributed data in ...
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Application of datamining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indica...
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ISBN:
(纸本)9780769530901
Application of datamining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indicate customers' interest in the product or purchasing behavior, a student's visits to a learning Management System (LMS) do not necessarily involve transfer of learning. Addressing such complexity in e-learning, this study analyzed students' log of a learning Management System (LMS) of two subjects at a university in Bangladesh, taught over six weeks duration. datamining and statistical tools have been used to rind relationships between students' LMS access behavior and overall performances. Results show that students having 'Low' access obtained poor grade, on campus access was higher than access from home. Background of students is very important for effective usage of web resources. Majority of the student considered LMS to be a quite helpful tool as teaching-learning method. Preparation and cleaning of the web-log files as well as application of datamining algorithms is important for learners' web usage analysis.
Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acqu...
详细信息
ISBN:
(纸本)9780769530901
Knowledge discovery and datamining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machinelearning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and datamining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and datamining, and Knowledge Discovery in database (K-DD) process are presented in the paper. datamining theory, datamining tasks, datamining technology and datamining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.
With the development of the technology of C,41, it is widely used in our English teaching. So many people focus their attentions on the influences of CAI in different aspects. Therefore, the author tries to investigat...
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ISBN:
(纸本)9780769530901
With the development of the technology of C,41, it is widely used in our English teaching. So many people focus their attentions on the influences of CAI in different aspects. Therefore, the author tries to investigate the influences of CAI English teaching pattern on the Autonomous learning. in divided class instruction of independent college. As far as it is concerned, it is important for us to find out the essential influences of" divided class instruction" on students in independent college, thus to make the reform of our teaching pattern successfully.
E-learning is most important by its role in distance teaching, and as supplementary learning material, it's mainly used in higher education area. Paper first introduced the significance of e-learning and applicati...
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ISBN:
(纸本)9780769530901
E-learning is most important by its role in distance teaching, and as supplementary learning material, it's mainly used in higher education area. Paper first introduced the significance of e-learning and application of constructive theory. In contrast with traditional education, its application possesses broad prospects. Then we made a systematic exposition of interrelated theory basis, discussed a new 3D virtual study pattern. Lastly, it presented two application systems that we developed One is a virtual audio-video multimedia teaching center which has good immersion feelings;another is a virtual music appreciation environment which has dynamic interaction performance.
This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machinelearning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michi...
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This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machinelearning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the in terpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretabitity-accuracy tradeoff for testpatterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for testpatterns. (C) 2006 Elsevier Inc. All rights reserved.
A Classification Association Rule (CAR), a common type of mined knowledge in datamining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined cla...
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ISBN:
(数字)9783540734994
ISBN:
(纸本)9783540734987
A Classification Association Rule (CAR), a common type of mined knowledge in datamining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an "antecedent double right arrow consequent-class" rule. Classification Association Rule mining (CARM) is a recent Classification Rule mining (CRM) approach that builds an Association Rule mining (ARM) based classifier using CARs. Regardless of which particular methodology is used to build it, a classifier is usually presented as an ordered CAR list, based on an applied rule ordering strategy. Five existing rule ordering mechanisms can be identified: (1) Confidence-Support-size -of-Antecedent (CSA), (2) size-of-Antecedent-Confidence-Support (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) chi(2) Testing. In this paper, we divide the above mechanisms into two groups: (i) pure "support-confidence" framework like, and (ii) additive score assigning like. We consequently propose a hybrid rule ordering approach by combining one approach taken from (i) and another approach taken from (ii). The experimental results show that the proposed rule ordering approach performs well with respect to the accuracy of classification.
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