共查询到18条相似文献,搜索用时 125 毫秒
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提出一种时间序列偏向最近模式匹配算法;这种算法通过定义一种偏向最近距离及采用倾斜时间窗口Haar小波变换高层数据表示方法,实现时间序列偏向最近模式无遗漏高效查询.理论分析与实验验证证明了该方法的有效性. 相似文献
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由于数据流具有无限、高速等特性,使得对数据流的查询处理往往不是面向整个数据流,而是把查询处理的范围限定在某个可操作的范围内,比如一个数据窗口。另一方面,通过数据摘要近似表达数据,也是数据流查询处理应对存储空间约束的常用策略。本文提出一种基于滑动窗口的数据流小波摘要构造算法,利用了窗口技术与数据摘要技术的优点。算法的基本思路是基于滑动窗口模型,将数据流划分成若干等宽基本窗口,每个基本窗口内数据进行小波分解与系数约简,从而形成滑动小波摘要窗口。为使窗口内数据摘要绝对重构误差最优,定义一个系数删减标准,采用贪心策略对窗口内小波系数逐步求精,从而获得最优绝对误差小波摘要。实际应用结果证明了算法的有效性。 相似文献
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《计算机应用与软件》2015,(11)
在数据流聚类算法中,滑动窗口技术可以及时淘汰历史元组、只关注近期元组,从而改善数据流的聚类效果。如果同时数据流流速无规律地随时间动态变化,原来单纯的滑动窗口技术在解决这类问题时存在缺陷,所以,在充分考虑了滑动窗口大小和数据流流速之间关系的前提下,提出了基于动态可调衰减滑动窗口的变速数据流聚类算法。该算法对历史元组和近期元组分别赋予一定的权重进行处理,然后依据数据流流速的不同函数改变窗口的大小,从而实现数据流的聚类。提出了该数据流聚类算法的数据结构——变异数据流聚类的数据结构。通过真实数据和模拟数据来构造动态变速数据流从而作为验证算法的原始数据。实验结果表明,与Clu Stream聚类算法相比,该方法具有较高的聚类质量、较小的内存开销和较少的聚类处理时间。 相似文献
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随着大数据时代的到来,网络上产生了大量非结构化文本数据流,这些文本数据流具有动态、高维、稀疏等特征。针对这些特点,首先将传统的AP算法及流式文本数据特征相结合,然后提出文本数据流聚类算法——OAP-s算法。该算法通过在AP算法上引入衰减因子,对聚类中心结果进行衰减,同时将当前时间窗口的聚类中心带入到下一时间窗口中进行聚类。针对OAP-s算法的不足,又提出了OWAP-s算法。该算法在OAP-s算法模型的基础上定义了加权相似度,并通过引入吸引度因子,使得历史聚类中心更具吸引性,得到更精确的聚类结果。同时,两种算法均采用滑动时间窗口模式,使算法既能体现数据流的时态特征,又能反映数据流的分布特征。实验结果表明,两种算法在聚类精确度、稳定性方面均高于OSKM算法,而且具有较好的伸缩性和可扩展性。 相似文献
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基于构造型神经网络引入一种新的数据流聚类相似性函数,并根据滑动窗口模型数据流聚类的特点,定义了平均覆盖和重叠覆盖等概念,进而提出基于构造型神经网络的滑动窗口模型数据流聚类算法.该算法可以降低计算量,提高聚类速度.大规模无线电监洲数据聚类实验验证了该算法的有效性. 相似文献
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Sliding window is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. The main idea behind a transactional sliding window is to keep a fixed size window over a data stream. The window size is kept constant by removing old transactions from the window, when new transactions arrive. Older transactions of window are removed irrespective to whether a significant change has occurred or not. Another challenge of sliding window model is determining window size. The classic approach for determining the window size is to obtain it from the user. In order to determine the precise size of the window, the user must have prior knowledge about the time and scale of changes within the data stream. However, due to the unpredictable changing nature of data streams, this prior knowledge cannot be easily determined. Moreover, by using a fixed window size during a data stream mining, the performance of this model is degraded in terms of reflecting recent changes. Based on these observations, this study relaxes the notion of window size and proposes a new algorithm named VSW (Variable Size sliding Window frequent itemset mining) which is suitable for observing recent changes in the set of frequent itemsets over data streams. The window size is determined dynamically based on amounts of concept change that occurs within the arriving data stream. The window expands as the concept becomes stable and shrinks when a concept change occurs. In this study, it is shown that if stale transactions are removed from the window after a concept change, updated frequent itemsets always belong to the most recent concept. Experimental evaluations on both synthetic and real data show that our algorithm effectively detects the concept change, adjust the window size, and adapts itself to the new concepts along the data stream. 相似文献
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Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper, we design a generalized dimension-reduction framework for recent-biased approximations, aiming at making traditional dimension-reduction techniques actionable in recent-biased time series analysis. The framework is designed in two ways: equi-segmented scheme and vari-segmented scheme. In both schemes, time series data are first partitioned into segments and a dimension-reduction technique is applied to each segment. Then, more coefficients are kept for more recent data while fewer kept for older data. Thus, more details are preserved for recent data and fewer coefficients are kept for the whole time series, which improves the efficiency greatly. We experimentally evaluate the proposed approach, and demonstrate that traditional dimension-reduction techniques, such as SVD, DFT, DWT, PIP, PAA, and landmarks, can be embedded into our framework for recent-biased approximations over streaming time series. 相似文献
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针对滑动变长窗口BIC算法冗余分割点多的问题,提出了基于小波子带平均能量方差和BIC的音频分割算法相结合。该算法用小波子带平均能量方差将连续音频流分割成音频段,然后用改进的滑动变长窗口BIC算法在音频段上检测声学改变点。实验表明,该算法取得了较好的分割效果,与滑动变长窗口的BIC算法相比,该算法的准确率、召回率和综合性能都得了提高。 相似文献
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基于动态特征提取和神经网络的数据流分类研究 总被引:1,自引:0,他引:1
为提高数据流分类的精确性和适应性,提出了一种新的数据流分类方法。该方法基于总体最小二乘法对数据流进行分段拟合,并将传统曲线分析算法——滑动窗口(SW)和在线数据分割(OSD)进行结合、改进,以可变滑动窗口算法实现对数据流的合理分割,提高趋势分析精度。在此基础上,对数据流进行动态特征提取和判断,并以神经网络对数据流特征进行模式识别,精确分类,进而对监控对象提供早期预警、状态评估和决策支持。实验结果表明,该方法能对数据流进行有效的动态特征描述,分类效果明显。 相似文献
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因树型结构的良好表达能力,在互联网中传输的信息流越来越多以树型结构形式存储。但由于流式数据的时效性,隐含在数据流中的知识会随着时间的推移发生改变。针对数据流场景下挖掘最近时间段内的频繁子树模式的问题,提出了一种滑动窗口模型下挖掘频繁子树模式算法——SWMiner算法,用于挖掘数据流下任意时刻窗口下所有的频繁子树模式。SWMiner算法使用基于前缀树的结构来压缩存储生成的树模式,并且使用trie merging机制有效地更新子树模式的支持度。实验结果表明,SWMiner算法在滑动窗口模型中的性能优于目前现有的常用算法,能有效地挖掘最近时间段内的频繁树模式。 相似文献
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In recent years, data stream mining has become an important research topic. With the emergence of new applications, the data we process are not again static, but the continuous dynamic data stream. Examples include network traffic analysis, Web click stream mining, network intrusion detection, and on-line transaction analysis. In this paper, we propose a new framework for data stream mining, called the weighted sliding window model. The proposed model allows the user to specify the number of windows for mining, the size of a window, and the weight for each window. Thus users can specify a higher weight to a more significant data section, which will make the mining result closer to user’s requirements. Based on the weighted sliding window model, we propose a single pass algorithm, called WSW, to efficiently discover all the frequent itemsets from data streams. By analyzing data characteristics, an improved algorithm, called WSW-Imp, is developed to further reduce the time of deciding whether a candidate itemset is frequent or not. Empirical results show that WSW-Imp outperforms WSW under the weighted sliding window model. 相似文献
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滑动窗口是一种对最近一段时间内的数据进行挖掘的有效的技术,本文提出一种基于滑动窗口的流数据频繁项挖掘算法.算法采用了链表队列策略大大简化了算法,提高了挖掘的效率.对于给定的阈值S、误差ε和窗口长度n,算法可以检测在窗口内频度超过Sn的数据流频繁项,且使误差在εn以内.算法的空间复杂度为O(ε-1),对每个数据项的处理和查询时间均为O(1).在此基础上,我们还将该算法进行了扩展,可以通过参数的变化得到不同的流数据频繁项挖掘算法,使得算法的时间和空间复杂度之间得到调节.通过大量的实验证明,本文算法比其它类似算法具有更好的精度以及时间和空间效率. 相似文献