Award for distinguished scientific early career contributions to psychology: Robert M. Nosofsky.

The Early Career Awards, given for the first time in 1974, recognize the large number of excellent young psychologists. Recipients of this award may not have held a doctoral degree for more than nine years. For purposes of this award, psychology has been divided into 10 areas: animal learning and behavior, comparative; developmental; health; cognition/human learning; psychopathology; behavioral and cognitive neuroscience; perception/motor performance; social; applied research; and individual differences. Five areas are considered each year, with areas rotated in two-year cycles. The citation, biography, and selected bibliography of Robert M. Nosofsky, one of the 1993 recipients of the Award for Distinguished Scientific Early Career Contributions to Psychology (Cognition/Human Learning), are presented here. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Award for distinguished contribution to education and training in psychology: Jill N. Reich.

Presents the citation, biography, and selected bibliography of Jill N. Reich, one of the 1993 recipients of the American Psychological Association's Award for Distinguished Contribution to Education and Training in Psychology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

适于复杂信息融合系统的近似联合概率数据关联算法

文中在B.Zhou提出的直接概率计算(DC)和近似概率计算(AC)算法基础上提出了一种新的近似多传感器多目标联合概率数据关联算法。近似概率法是以一个目标为中心的近似聚为构造互联事件的起点，并在计算中将DC和AC结合得到的一种全邻的点迹—航迹关联算法，它能有效地提高目标点迹—航迹的关联正确率，在计算时耗上较完全联合概率法快得多，能满足工程中实时性的要求。将其在杂波下目标密集、航迹复杂的数据融合系统中进行实验，对关联正确率，关联耗时等与最近邻法进行了比较，效果较好。     

Distinguished professional contributions to public service: Edwin Shneidman.

Presents the citation, biography, and selected bibliography for Edwin Shneidman, one of the 1987 recipients of the American Psychological Association's Award for Distinguished Professional Contributions to Public Service. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

President’s column.

This presidential column announces the significant internal changes of Division 13 of the Consulting Psychology Journal. The journal is now published by the Educational Publishing Foundation, which is associated with the American Psychological Association in order to increase the quality, quantity, and diversity of submissions to the journal. The author outlines the steps that will be taken to make the Division a more viable and inviting organization for members in ways that are obvious and easily accessible to them. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Designing Templates for Mining Association Rules

Current approaches to data mining usually address specific userrequests, while no general design criteria for the extraction of associationrules are available for the end-user. In this paper, we propose aclassification of association rule types, which provides a general frameworkfor the design of association rule mining applications. Based on theidentified association rule types, we introduce predefined templates as ameans to capture the user specification of mining applications. Furthermore,we propose a general language to design templates for the extraction ofarbitrary association rule types.     

An efficient algorithm for mining frequent inter-transaction patterns

In this paper, we propose an efficient method for mining all frequent inter-transaction patterns. The method consists of two phases. First, we devise two data structures: a dat-list, which stores the item information used to find frequent inter-transaction patterns; and an ITP-tree, which stores the discovered frequent inter-transaction patterns. In the second phase, we apply an algorithm, called ITP-Miner (Inter-Transaction Patterns Miner), to mine all frequent inter-transaction patterns. By using the ITP-tree, the algorithm requires only one database scan and can localize joining, pruning, and support counting to a small number of dat-lists. The experiment results show that the ITP-Miner algorithm outperforms the FITI (First Intra Then Inter) algorithm by one order of magnitude.     

Summary report of journal operations: 1984.

Provides the summary report for the American Psychological Association's journal operations. The summary here is compiled from the 1984 annual reports of the Council of Editors and from Central Office records. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

大型数据集的多维变量关联性挖掘

为了探索多维数据集中各变量之间的关系,特别是非函数关系,对数据集所在的n维立方体的各个维度进行划分,在得到的n维网格中定义自然概率密度函数,以此得到数据集在特定划分下的互信息值。对所有的划分取互信息最大值,正态化后即为所定义的特征张量的一项,取特征张量在给定最大网格数下的最大项的值定义为MIC,它度量了多维变量间的关联程度。