An Associative Memory for Association Rule Mining
An Associative Memory for Association Rule Mining
复制标题
关联规则挖掘的联想记忆
DOI：
10.1109/ijcnn.2007.4371304
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Simon E. M. O'Keefe
中科院分区：
文献类型：
--
作者：
Vicente Oswaldo Baez Monroy;Simon E. M. O'Keefe
关联规则挖掘是数据挖掘中一个深入研究的问题。它的解决方案旨在通过基于不同策略的方法来实现，例如，使用新颖的数据结构来表示所发现的知识，转换输入数据以加速过程，利用项集的属性，或者最优地遍历可能的项集搜索空间，或者形成用于生成相应的最终项集的频繁项集的紧凑表示。规则，以及其他。令人惊讶的是，生物启发的方法很少被提出。在这项工作中，我们专注于调查，如果一种类型的映射神经网络，更好地称为关联记忆，是适合于关联规则挖掘。特别是，我们的目标是确定是否可以估计项集支持嵌入在一个训练有素的联想记忆的权重矩阵的知识，以产生进一步的关联规则，从这样的知识。
Association rule mining is a thoroughly studied problem in data mining. Its solution has been aimed for by approaches based on different strategies involving, for instance, the use of novel data structures to represent the knowledge discovered, the transformation of the input data to speed up the process, the exploitation of the itemset properties either to traverse the possible itemset search space optimally or to form compact representation of the frequent itemsets employed for the generation of the corresponding final rules, and others. Surprisingly, biologically-inspired approaches have rarely been proposed. In this work, we focus on investigating if a type of mapping neural network, better known as an associative memory, is suitable for association rule mining. In particular, our aim is to determine if itemset support can be estimated from the knowledge embedded in the weight matrix of a trained associative memory in order to generate further association rules from such a knowledge.
尝试利用 T 结肠造影中大肠突起病变的分形分析来诊断恶性肿瘤
DOI：
--
发表时间：
2022
期刊：
影响因子：
--
作者：
間宮悠;栃木透
通讯作者：
栃木透