A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial partic...A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.展开更多
Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidat...Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.展开更多
云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布...云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布特性,这一特性不利于精确查询,但可为关联规则的挖掘提供便利条件.首先,依据泛化值之间可能的相交或包含关系,将泛化值进行分层聚类,为了保存与不确定数据集挖掘相关的重要信息,给出了构建不确定频繁模式树的算法,在此基础上,提出了频繁项集挖掘子算法(data mining algorithm for uncertain frequent item-sets,UFI-DM)和关联规则生成子算法(algorithm for generating association rules,GAR),分别用于挖掘频繁项集和生成关联规则,最后,通过理论分析和实验比对,论证了算法的可行性和有效性.展开更多
文摘A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.
基金theFundoftheNationalManagementBureauofTraditionalChineseMedicine(No .2 0 0 0 J P 5 4 )
文摘Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.
文摘云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布特性,这一特性不利于精确查询,但可为关联规则的挖掘提供便利条件.首先,依据泛化值之间可能的相交或包含关系,将泛化值进行分层聚类,为了保存与不确定数据集挖掘相关的重要信息,给出了构建不确定频繁模式树的算法,在此基础上,提出了频繁项集挖掘子算法(data mining algorithm for uncertain frequent item-sets,UFI-DM)和关联规则生成子算法(algorithm for generating association rules,GAR),分别用于挖掘频繁项集和生成关联规则,最后,通过理论分析和实验比对,论证了算法的可行性和有效性.
