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    • 31. 发明授权
    • Methods of creating a dictionary for data compression
    • 创建数据压缩字典的方法
    • US08037034B2
    • 2011-10-11
    • US11781833
    • 2007-07-23
    • Piotr M. PlachtaWolfram SauerBalakrishna Raghavendra IyerSteven Wayne White
    • Piotr M. PlachtaWolfram SauerBalakrishna Raghavendra IyerSteven Wayne White
    • G06F7/00G06F17/00
    • H03M7/3088
    • Some aspects of the invention provide methods, systems, and computer program products for creating a static dictionary in which longer byte-strings are preferred. To that end, in accordance with aspects of the present invention, a new heuristic is defined to replace the aforementioned frequency count metric used to record the number of times a particular node in a data tree is visited. The new heuristic is based on counting the number of times an end-node of a particular byte-string is visited, while not incrementing a count for nodes storing characters in the middle of the byte-string as often as each time such nodes are visited. The result is an occurrence count metric that favors longer byte-strings, by being biased towards not incrementing the respective occurrence count values for nodes storing characters in the middle of a byte-string.
    • 本发明的一些方面提供用于创建静态词典的方法,系统和计算机程序产品,其中优选较长的字节串。 为此,根据本发明的方面,定义新的启发式来代替用于记录数据树中的特定节点被访问次数的上述频率计数度量。 新的启发式是基于对特定字节串的端节点进行访问的次数进行计数,而不会在每次访问这些节点时频繁地在字节串中间存储字符的节点递增计数 。 结果是有利于较长字节串的发生计数度量,偏向于不增加在字节串中间存储字符的节点的相应出现计数值。
    • 34. 发明授权
    • Using object relational extensions for mining association rules
    • 使用对象关系扩展来挖掘关联规则
    • US06301575B1
    • 2001-10-09
    • US09191424
    • 1998-11-12
    • Atul ChadhaBalakrishna Raghavendra IyerKarthick Rajamani
    • Atul ChadhaBalakrishna Raghavendra IyerKarthick Rajamani
    • G06F1730
    • G06F17/30539Y10S707/99932
    • A method, apparatus, and article of manufacture for computer-implemented use of object relational extensions for mining association rules. Data mining is performed by a computer to retrieve data from a data store stored on a data storage device coupled to the computer. A multi-column data store organized using a multi-column data model is received. One of the columns in the multi-column data store represents a transaction, and each of the remaining columns in the multi-column data store represents elements of that transaction. A combination operator is performed to obtain candidate itemsets of data from the multi-column data store, each itemset being a combination of a number of rows of the multi-column data store. Large itemsets of data are generated from the candidate itemsets, wherein each itemset has at least a minimum support. Association rules are generated from the large itemsets of data, wherein each association rule has at least a minimum confidence.
    • 用于采矿关联规则的对象关系扩展的计算机实现使用的方法,装置和制品。 数据挖掘由计算机执行以从存储在耦合到计算机的数据存储设备上的数据存储器中检索数据。 接收使用多列数据模型组织的多列数据存储。 多列数据存储中的列之一表示事务,多列数据存储中的其余列表示该事务的元素。 执行组合运算符以从多列数据存储获得数据的候选项集,每个项集是多列数据存储的多行的组合。 从候选项集中生成大量数据项,其中每个项集具有至少最小支持。 关联规则是从大量数据集生成的,其中每个关联规则具有至少最小的置信度。