| 词汇 | example_english_data-mining |
| 释义 | Examples of data miningThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. Numerous tools suitable for datamining in biology are available, yet the selection of an appropriate tool is non-trivial. So the important question is, how can we measure the effectiveness of classification results in datamining? His research interests include machine learning, genetic algorithms, datamining, and network security. In the following, we illustrate the potential of sparse grids for problems of a higher dimensionality in the context of numerical quadrature and datamining. However, the rules generated by the datamining methods are rarely used to provide greater insight into the original problem domain. Subjective techniques generally operate by comparing the beliefs of a user against the patterns discovered by the datamining algorithm. These goals can be achieved by using various general datamining methods, which are described below. In datamining and machine learning communities, several classification algorithms have been proposed, where most of these produce classifiers with an acceptable error rate. The range of applications spans from numerical programming, symbolic computing, computer vision, to telephony and datamining. As her research progressed, she used more complex case representations, reasoning and learning strategies, and datamining techniques for pattern recognition. The majority of the datamining literature concentrates on the discovery of accurate and comprehensible patterns. We are exploring the use of fuzzy datamining and concepts introduced by the semantic web to operate in synergy to perform distributed intrusion detection. On the positive side, datamining and machine learning can be used to generalise news reports, and thereby generate knowledge for coherence systems. While the term ' datamining' is relatively recent, techniques for identifying patterns in data are much older. This review provides a discussion of and pointers to efficient algorithms for the common datamining tasks in a mathematical framework. These properties make them well suited to datamining. This, however, does not mean that radial basis functions cannot be used in datamining. For example, there will be increasing interest in the possibility of datamining in banks of neuroimages. His main research interests include information visualization, context awareness, and datamining. This selection includes some of the most widely used datamining problems such as association rule mining, predictive models, and clustering. One datamining task is indeed the identification of features containing information that can contribute to a particular research question. This kind of navigation is helpful for the feature selection or the choice of datamining tools. The principle, algorithms and knowledge representation of some function models of datamining are described. In this section we describe two practical goals of datamining: prediction and description. The goals of prediction and description can be achieved using a variety of particular datamining methods. The relative importance of prediction and description on particular datamining applications can vary considerably. For classification problems in datamining, we can look at a precision class by class or globally. Speed is necessary for applications such as datamining, dealing with huge amount of texts. As these methods are commonly used for computer identification of, for example, fraud detection, importance is given to the statistical significance of datamining results. Ideas and algorithms from numerical linear algebra are important in several areas of datamining. In addition to this, the most relevant practical experiences of developing a datamining tool for a specific real-world domain are shared. The goal of datamining is then to uncover interesting structures or aspects of the underlying probability distribution. We believe that datamining does provide many new and challenging questions for approximation theory, stochastic analysis, numerical analysis and parallel algorithm design. They state that in practical applications, datamining is based on two assumptions. The statistical approach to datamining is most widely used for practical datamining applications because real-world data is commonly associated with uncertainty. Notions of a 'semantic web' or of 'datamining' depend on these dynamic functions becoming quicker and more reliable. Statistical models appear in statistics obviously, in artificial intelligence, machine learning and datamining, and in specific application areas. The essential task for datamining algorithms is to search for patterns that are 'surprising' in addition to being accurate and comprehensible. Predicting the class labels of test objects is the primary aim for classification in datamining. Furthermore, the common evaluation criteria used for induced knowledge in the context of developing intelligent reasoning systems and datamining have been addressed. The initial phase uses datamining techniques to analyse data streams that capture process, system and network states and detect anomalous behaviour. With regard to datamining, a huge amount of data is collected every day in the form of event-time sequences. Testing complex temporal relationships involving multiple granularities and its application to datamining. The data constitutes a training set that may contain hundreds or thousands of examples, or even, when the term "datamining" is used, some millions. The hidden knowledge locked away in corporate data stores has a great deal of potential that can theoretically be uncovered by datamining. The datamining and analytic functions of personalisation software provide companies with marketing knowledge required for managing customer databases. The four main types of clustering algorithm used in datamining are as follows. His interests are in the field of spatio-temporal databases and datamining. The most practical way of carrying out datamining is to do it on personal computers with limited resources. Figure 1 presents a simplified overall system structure for the case of hot strip mill datamining. She is working on research projects mostly in scientific datamining. His research interests include evolutionary algorithms, genetic programming, open-ended computational synthesis, datamining, and bioinformatics. Moreover, the application of social simulation models to datamining, robotics and process control provides a test of social theories and modelling procedures. However, regression analysis can also be used as a datamining tool. Regression analysis can be the goal of a datamining exercise. There is no universal datamining method and choosing a particular algorithm for a particular application is an art rather than a science. As stated in the last section, objective interestingness measures may not highlight the most important patterns produced by the datamining system. The second category of tools involves natural language processing, machine learning and datamining. Of course, both implicit and explicit maximization of predictability are forms of data snooping or datamining and may bias classical statistical inferences. In this section we concentrate on interestingness measures for association rules, as they are so important and widespread within the datamining community. They are an extremely important and widely used datamining technique that is computationally efficient. Regression is another important issue in datamining. Nonmonotony in datamining is dealt with by concept hierarchy and layered mining. Roughly speaking, a classifier in datamining is constructed from labelled data records, and later is used to forecast classes of previously unseen data as accurately as possible. This article presents a review of the available literature on the various measures devised for evaluating and ranking the discovered patterns produced by the datamining process. An important goal of educational datamining is to discover what helps - specifically, which tutor and student actions help which students learn which skills in which contexts. The applicability of the overall approach has been verified by using the developed tool for datamining on a hot strip mill of a steel plant. Hence, our experience of practical datamining suggests that the fully efficient use of any data mining tool requires the user to master the data mining methods involved. Only in recent years has there been an increased interest among the numerical linear algebra community in tensor computations, especially for applications in signal processing and datamining. In fact many datamining techniques such as clustering depend on similarity measures between objects. This shows that datamining must pay close attention to computational efficiency and simple models are often preferred. The science of extracting useful information from large data sets is usually referred to as 'datamining', sometimes along with 'knowledge discovery'. The software tool of datamining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma. In addition to discussing a vast collection of datamining research it also provides a database perspective. This is where the challenge to the datamining community lies and involves the development of interestingness measures and the integration of the user within the system. The understanding of the limits of datamining methods represents one of the major benefits of this approach. A bibliography of temporal, spatial and spatio-temporal datamining research. Emerging datamining/semantic search tools. This paper has reviewed and evaluated the current research literature on the various techniques for determining the interestingness of patterns discovered by the datamining process. Related to the general problem of rewriting queries, there is the problem of rewriting queries using views, that is particularly relevant in datamining and data warehouse contexts. The elements of statistical learning: datamining, inference, and prediction. It is interesting to note that several of the linear algebra ideas used in datamining were originally conceived in applied statistics and data analysis, especially in psychometrics. Typical datamining tasks are as follows. The talk concerned the embedding of datamining techniques within deductive query systems. The practical aspects of datamining include dealing with issues such as data storage and access, scalability of massive data sets, presentation of results and human-machine interaction. Moral foundations of datamining. Autonomous decision-making: a datamining approach. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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