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Titlebook: Knowledge Discovery in Databases: PKDD 2007; 11th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007

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發(fā)表于 2025-3-25 06:04:51 | 只看該作者
Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction sense that facts have to be mentioned in a similar manner in order to be generalized to a textual pattern. Data sparseness thus becomes a problem when trying to extract information from hardly redundant sources like corporate intranets, encyclopedic works or scientific databases..We present results
22#
發(fā)表于 2025-3-25 07:29:47 | 只看該作者
A Graphical Model for Content Based Image Suggestion and Feature Selections expressed as queries. From the importance of the visual information in many applications such as advertisements and security, we motivate in this paper, the .. It targets the user’s long term needs as a recommendation of products based on the user preferences in different situations, and on the vi
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發(fā)表于 2025-3-25 12:16:15 | 只看該作者
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發(fā)表于 2025-3-25 18:56:39 | 只看該作者
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發(fā)表于 2025-3-25 20:48:37 | 只看該作者
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發(fā)表于 2025-3-26 00:16:56 | 只看該作者
27#
發(fā)表于 2025-3-26 05:31:48 | 只看該作者
An Algorithm to Find Overlapping Community Structure in Networkshese only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorithm based on the . centrality measure. Like the original algorith
28#
發(fā)表于 2025-3-26 11:28:14 | 只看該作者
Privacy Preserving Market Basket Data Analysis preserving market basket data analysis was solely focused on support/ confidence framework. Since there are inherent problems with the concept of finding rules based on their support and confidence measures, many other measures (e.g., correlation, lift, etc.) for the general market basket data anal
29#
發(fā)表于 2025-3-26 14:08:52 | 只看該作者
Feature Extraction from Sensor Data Streams for Real-Time Human Behaviour Recognition the routine motion behaviour of the subject (i.e. places visited, routes taken between places) we show it is possible to detect unusual behaviours while they are happening. This requires the processing of continuous sensor data streams, and real-time recognition of the subject’s behaviour. To addre
30#
發(fā)表于 2025-3-26 19:57:28 | 只看該作者
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