| 1. | Frequent pattern mining technology in data mining is for mining characteristic patterns with frequent occurrences among data
数据挖掘中的频繁模式挖掘技术专注于发现数据中频繁出现的特征模式。 |
| 2. | Introduce searching engine technology of " event inspection " and define the modular description language in respect of its intrusion patterns , as well develop the corresponding rules parser
采用了“事件检测”引擎技术，并定义了一套入侵特征模式描述语言及相应的规则语法解释器。 |
| 3. | After that , we designed a new data model , called inter - related successive trees irst , to find frequent patterns from multiple time series without generation lots of candidate patterns
在挖掘算法实现上，根据序列特征模式的有序性和重复性，提出了一种无须生成大量的候选模式集的互关联后继树挖掘算法。 |
| 4. | So we can utilize the special sensor data to create the currency feature collection via analyzing currency samples in statistics . the fake currency can be identified rapidly and exactly by this method
利用特殊的传感器数据，通过对货币样本的统计分析建立货币的特征模式集，实现对真伪的快速、准确识别。 |
| 5. | Therefore new attacks can be logged in modular description language and added to the attacks logging database . the description languages together with the event inspection function make the system ' s expandability and upgrading possible . 3
这样，可以用模式描述语言描述新的攻击并增加到攻击特征模式库中，同时增加新的事件检测引擎来识别此攻击，保证了系统的可扩展性要求，方便升级。 |
| 6. | What we do in this paper goes as : 1 . we study and analysis the mathematic background of related basic algorithms about hydrological time - series data mining , including the typical algorithms of data pretreatment , characteristic mode mining and comparability measurement 2
本文主要的工作如下： ( 1 )对水文时间序列数据挖掘相关基本算法进行了分析研究，包括数据预处理、特征模式挖掘和相似性度量等典型的基本算法。 |
| 7. | It induces the idea of pixel ' s 8 - neighbor fourier series , randomly chooses the local region of texture and computes the spectrum of it . after quantizing the spectrum , the spectrum histogram of the local area is extracted and then provided to the sofm network as a feature vector to train the net
引入象素的八近邻付立叶级数，计算随机选取的纹理区域的纹理谱并量化得到谱直方图，将其作为sofm网络的特征模式输入并训练网络。 |
| 8. | After that , we designed a new data model , called inter - related successive trees irst , to find frequent patterns from multiple time series without generation lots of candidate patterns . experiment illustrates that the method is simpler and more flexible , efficient and useful , compared with the previous methods
在挖掘算法实现上，根据序列特征模式的有序性和重复性，提出了一种无须生成大量的候选模式集的互关联后继树挖掘算法，极大地提高了挖掘效率。 |
| 9. | These researches will help us to discover changing or developing principle of things , support to decision - making , etc . the thesis addresses several key technical problems of pattern mining and its search based similarity in time series , which covers feature patterns and relationship patterns mining , pattern search based similarity in time series and stream time series and issues concerning application system implementation oriented to analysis . major contributions of this thesis include : 1 . research of mining feature patterns in time series a novel method is proposed to discovery frequent pattern from time series
本文在分析时间序列特点和实际应用需求的基础上，针对时间序列的挖掘与相似性查找一些关键技术进行了研究，具体包括特征模式挖掘、多序列关联模式挖掘、相似性模式查找等方面，所做的工作和取得的创新成果体现在以下三个方面： 1 )时间序列特征模式挖掘研究首次提出了一种基于互关联后继树模型的时序特征模式挖掘方法。 |
| 10. | According to the complexity of pattern , the mined characteristic patterns could be sorted as frequent item , frequent sequence and frequent sub tree , etc . the paper builds tree - model of rna molecules and utilizes frequent sub tree mining algorithm to mine common topological patterns among rna secondary structures
根据特征模式复杂性,可分为频繁项模式、频繁序列模式以及频繁子树模式等。本文对rna分子建立树形模型,利用频繁子树挖掘算法挖掘rna二级结构中的公共拓扑模式。 |
