排序方式: 共有34条查询结果,搜索用时 15 毫秒
1.
频繁模式树算法是一种优秀的关联规则挖掘算法.频繁模式树算法的挖掘对象是水平数据分布的数据库,现实中有大量数据垂直分布的数据库不能直接应用频繁模式树算法进行挖掘.本文针对垂直数据分布的数据库,提出一种有效的频繁模式树生长算法,只需两次数据库扫描,即可生成相应的频繁模式树. 相似文献
2.
一种时间序列频繁模式挖掘算法及其在WSAN行为预测中的应用 总被引:1,自引:0,他引:1
该文提出FPM(Frequent Pattern Mining)算法充分考虑频繁模式在时间序列中出现次数和分布。基于这些不同分布的频繁模式扩展MAMC(Mixed memory Aggregation Markov Chain)模型提出FMAMC(Frequent pattern based Mixed memory Aggregation Markov Chain)模型。将FPM和FMAMC应用到实际的智能楼宇项目中,证明和现有算法相比FPM算法具有较好的时间性能,FMAMC模型能够比MAMC模型更准确的预测WSAN节点行为。 相似文献
3.
Given a sample of binary random vectors with i.i.d. Bernoulli(p) components, that is equal to 1 (resp. 0) with probability p (resp. 1−p), we first establish a formula for the mean of the size of the random Galois lattice built from this sample, and a more complex one for its variance. Then, noticing that closed α-frequent itemsets are in bijection with closed α-winning coalitions, we establish similar formulas for the mean and the variance of the number of closed α-frequent itemsets. This can be interesting for the study of the complexity of some data mining problems such as association rule mining, sequential pattern mining and classification. 相似文献
4.
《Journal of Visual Communication and Image Representation》2014,25(2):329-338
Due to the exponential growth of the video data stored and uploaded in the Internet websites especially YouTube, an effective analysis of video actions has become very necessary. In this paper, we tackle the challenging problem of human action recognition in realistic video sequences. The proposed system combines the efficiency of the Bag-of-visual-Words strategy and the power of graphs for structural representation of features. It is built upon the commonly used Space–Time Interest Points (STIP) local features followed by a graph-based video representation which models the spatio-temporal relations among these features. The experiments are realized on two challenging datasets: Hollywood2 and UCF YouTube Action. The experimental results show the effectiveness of the proposed method. 相似文献
5.
在电子对抗中,截获到对方的通信比特流序列之后,当链路协议类型未知时,现有的协议解析工具往往无法分析比特流所承载的有用信息。为了获取比特流承载信息,首先需要切分比特流得到链路帧。该文根据链路帧结构的一般规律,提出一种基于数据挖掘的比特流切分算法。通过频繁序列统计、关联规则分析以及关联规则整合,识别出比特流中标识帧起始的多重关联规则序列。测试结果表明,该算法能够从未知比特流中提取有效的切分标识,正确实现比特流切分。与同类基于数据挖掘的比特流分析方法相比,该算法复杂度低,输出结果唯一且可信度高。 相似文献
6.
B 《电子学报:英文版》2021,30(2):258-267
Frequent subgraph mining (FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been devel-oped with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing (PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, gSpan and Gaston. We compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38%depending on the dataset. 相似文献
7.
现有信任网络研究大多侧重于信任的推理及聚合计算,缺乏对实体重要性及其关联性分析,为此该文提出一种多维信任序列模式(Multi-dimensional Trust Sequential Patterns, MTSP)挖掘算法。该算法包括频繁信任序列挖掘和多维模式筛选两个处理过程,综合考虑信任强度、路径长度和实体可信度等多维度因素,有效地挖掘出信任网络中的频繁多维信任序列所包含的重要实体及其关联结构。仿真实验表明该文所提MTSP算法的挖掘结果全面、准确地反映了信任网络中重要信任实体关联性及其序列结构特征。 相似文献
8.
本文结合生活中变电站PT熔断器的实际应用,着重对PT熔断器频繁烧毁的原因和解决措施进行分析,为我国电力资源有效运用提供理论参考. 相似文献
9.
10.