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The following topics are dealt with: database and data mining; Internet and Web applications; high performance computing; networking and mobile computing; multimedia and computer graphics; image processing; computer vision and video surveillance; ubiquitous and sensor networks; VLSI and computer systems; information systems; software engineering; service oriented computing.
The data lifecycle means the process from datapsilas advent to its disappearance; the data management runs through the whole data lifecycle. Data management aims at providing the data which are complete, accurate and conducts time effect for userspsila data retrieval. At each stage of datapsilas life cycle, different demands exist upon data management, among which data store management is the most...
Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken both classification ability and cost-sensitive into account well. In this paper, we present a new strategy for attributes selection,...
Event detection and recognition is a major task in ACE evaluation plan. In this paper, we focus on solving the two subtasks: (1) event detection and classification, (2) their argument role identification. For the first subtask, the strategy of local feature selection and explicit discrimination of positive and negative features is used in order to ensure the performance of each type. For the second...
The KDB-tree is a traditional point access method for retrieving multidimensional data. Many literatures frequently address the low storage utilization and insufficient retrieval performance as two bottlenecks for KDB-tree family of structures. A large amount of unnecessary splits caused by data insertion orders and data skewness is the fatal reason for these two bottlenecks. Compressing KDB-trees...
The association rule has become one of the most important techniques in data mining. New algorithms must be developed in order to apply it to more areas. This paper proposes association rule algorithms for logical equality relationships, modified from the original Apriori and FP-Growth algorithms. Logical equality is defined as truerarrtrue (1rarr1) or falserarrfalse (0rarr0) associations. This special...
This paper discuss the property of symmetric data interpolation surface. For the given data, when the parameters very, the interpolating surface varies, so the interpolating surfaces can be modified by selecting suitable parameters under the condition that the interpolation data are not changed. The problem is that when the two parameters vary, how does the interpolating surface change. The discussion...
Mining closed frequent itemsets in the sliding window is one of important topics of data streams mining. In this paper, we propose a novel algorithm, FPCFI-DS, which mines closed frequent itemsets in the sliding window of data streams efficiently, and maintains the precise closed frequent itemsets in the current window at any time. The algorithm uses a single-pass lexicographical-order FP-Tree-based...
Clustering is for many years now one of the most complex and most studied problems in data mining. Until now the most commonly used algorithm for finding groups of similar objects in large databases is CURE. The main advantage of CURE, compared to other clustering algorithms, is its ability to identify non spherical or rectangular shaped objects. In this paper we present a new algorithm called CUZ...
The main reason of current robotics research is the difficulties of coping with dynamic environment, uncertainty, operational limitation, etc. Human robot interaction (HRI) becomes important role in robotics research and especially humanpsilas roles in HRI is increasing. In this paper, we made robot task planning framework for MI HRI in home service environment. We suggest an structure of task network...
Bagging has been known to be successful in increasing the accuracy of prediction of the unstable classifiers. In bagging predictors are constructed using bootstrap samples from the training sets and then aggregated to form a bagged predictor. The robust bagging discard the bootstrapped classifiers generating extreme error rates, as estimated by the out-of-bag error rate and to combine over the remaining...
In this paper, after discussing classical rough set theory based on indiscemibility relation and the problem of its reduction algorithms on Seismology Data, we arrived at a new definition of dominance distinguishing matrix by taking advantage of the unique characteristic of the extended model. Then the corresponding reduction and rule extracting algorithms are accordingly presented to enhance the...
Identifying the pattern support distribution (PSD) in datasets is useful for many data mining tasks, such as market basket analysis. The support of a pattern is the frequency of its occurrence in a dataset. Calculating the distribution of these supports over an entire dataset is computationally expensive; this cost can be reduced by sampling from the dataset and computing the PSD on a relatively small...
A variety of cache replacement algorithms have been proposed and applied in different situations, in which the LRU (least recently used) and LFU (least frequently used) replacement policies are two of the most popular policies. However, most real systems donpsilat consider obtaining a maximized throughput by switching between the two policies in response to the access pattern. In this paper, we propose...
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