全文获取类型
收费全文 | 596篇 |
免费 | 89篇 |
国内免费 | 87篇 |
专业分类
电工技术 | 6篇 |
综合类 | 31篇 |
化学工业 | 1篇 |
金属工艺 | 1篇 |
机械仪表 | 2篇 |
建筑科学 | 1篇 |
矿业工程 | 1篇 |
能源动力 | 1篇 |
轻工业 | 14篇 |
石油天然气 | 1篇 |
无线电 | 57篇 |
一般工业技术 | 8篇 |
自动化技术 | 648篇 |
出版年
2023年 | 2篇 |
2022年 | 2篇 |
2021年 | 7篇 |
2020年 | 9篇 |
2019年 | 6篇 |
2018年 | 13篇 |
2017年 | 17篇 |
2016年 | 15篇 |
2015年 | 28篇 |
2014年 | 31篇 |
2013年 | 19篇 |
2012年 | 59篇 |
2011年 | 61篇 |
2010年 | 57篇 |
2009年 | 68篇 |
2008年 | 70篇 |
2007年 | 68篇 |
2006年 | 66篇 |
2005年 | 57篇 |
2004年 | 49篇 |
2003年 | 32篇 |
2002年 | 14篇 |
2001年 | 9篇 |
2000年 | 8篇 |
1999年 | 3篇 |
1998年 | 1篇 |
1997年 | 1篇 |
排序方式: 共有772条查询结果,搜索用时 15 毫秒
1.
Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms. 相似文献
2.
基于Apriori算法改进的关联规则提取算法 总被引:11,自引:2,他引:9
通过对Apriori算法的基本思想和性能的研究分析,认为Apriori算法存在一些不足。并且根据这些不足提出了相应的改进算法对Apriori算法进行优化,从而得到一种改进的Apriori算法,与原算法相比运算效率大大提高。 相似文献
3.
4.
《Expert systems with applications》2014,41(6):2703-2712
A concept lattice is an ordered structure between concepts. It is particularly effective in mining association rules. However, a concept lattice is not efficient for large databases because the lattice size increases with the number of transactions. Finding an efficient strategy for dynamically updating the lattice is an important issue for real-world applications, where new transactions are constantly inserted into databases. To build an efficient storage structure for mining association rules, this study proposes a method for building the initial frequent closed itemset lattice from the original database. The lattice is updated when new transactions are inserted. The number of database rescans over the entire database is reduced in the maintenance process. The proposed algorithm is compared with building a lattice in batch mode to demonstrate the effectiveness of the proposed algorithm. 相似文献
5.
Manuel Baena-García José M. Carmona-Cejudo Rafael Morales-Bueno 《Journal of Computer and System Sciences》2014
Discovering frequent factors from long strings is an important problem in many applications, such as biosequence mining. In classical approaches, the algorithms process a vast database of small strings. However, in this paper we analyze a small database of long strings. The main difference resides in the high number of patterns to analyze. To tackle the problem, we have developed a new algorithm for discovering frequent factors in long strings. We present an Apriori-like solution which exploits the fact that any super-pattern of a non-frequent pattern cannot be frequent. The SANSPOS algorithm does a multiple-pass, candidate generation and test approach. Multiple length patterns can be generated in a pass. This algorithm uses a new data structure to arrange nodes in a trie. A Positioning Matrix is defined as a new positioning strategy. By using Positioning Matrices, we can apply advanced prune heuristics in a trie with a minimal computational cost. The Positioning Matrices let us process strings including Short Tandem Repeats and calculate different interestingness measures efficiently. Furthermore, in our algorithm we apply parallelism to transverse different sections of the input strings concurrently, speeding up the resulting running time. The algorithm has been successfully used in natural language and biological sequence contexts. 相似文献
6.
传统的频繁核心项集挖掘需多次生成和反复扫描数据库,导致生成效率低下。为此,提出一种快速生成频繁核心项集算法FMEP。该算法使用Rymon枚举树作为搜索空间,并采用分而治之的策略选择特定的路径进行剪枝。利用频繁核心项集特有的反单调性质,可以快速地判断某一个候选项集是否为频繁核心项集,而无需和所有直接子集的析取支持度进行比较。通过上述方法,可以达到快速挖掘的目的。实验结果证明,该算法能够在挖掘出所有的频繁核心项集精简表示元素的同时,降低消耗时间,与MEP算法相比,在密集型数据集上的时间可缩短2倍以上,在稀疏型数据集上时间至少缩短30%。 相似文献
7.
Laszlo Szathmary Petko Valtchev Amedeo Napoli 《International Journal of Software and Informatics》2010,4(3):219-238
Rare association rules correspond to rare, or infrequent, itemsets, as opposed
to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities
of local, rather than global, scope that can nevertheless provide valuable insights
to an expert, especially in areas such as genetics and medical diagnosis where some specific
deviations/illnesses occur only in a small number of cases. The work presented here is motivated
by the long-standing open question of efficiently mining strong rare rules, i.e., rules
with high confidence and low support. We also propose an efficient solution for finding the
set of minimal rare itemsets. This set serves as a basis for generating rare association rules. 相似文献
8.
为了挖掘到有价值的信息,需要挖掘多维数据流上的频繁项目集,因此引入多维项目和多维项目集的概念表示多维数据流上的项目.设计了一种紧凑、压缩的数据结构MaxFP-Tree用于维护多维项目集,并在MaxFP-Tree的基础上设计了挖掘多维数据流上最大频集的增量式更新算法.实验结果表明,设计的挖掘多维数据流中最大频集的模型和算法是高效的. 相似文献
9.
数据流中基于滑动窗口的最大频繁项集挖掘算法* 总被引:2,自引:0,他引:2
挖掘数据流中最大频繁项集是从数据流中获得信息的一种有效手段,是数据流挖掘研究的热点之一。结合数据流的特点,提出了一种新的基于滑动窗口的最大频繁项集挖掘算法。该算法用位图来存储数据流中流动的数据;采用直接覆盖的方法存储和更新数据流上的数据;在深度优先搜索挖掘最大频繁项集时,除采用经典的剪枝策略外,还提出了与父等价原理相对应的子等价剪枝策略;最后将挖掘结果存储在索引链表中以提高超集检测效率,进一步减少挖掘最大频繁项集的时间。理论分析和实验结果证实了该算法在时间和空间上的有效性。 相似文献
10.