Out of the ghetto: Integrating Catholics into mainstream psychology in the United States after World War II.

The American Catholic Psychological Association (ACPA) was a voluntary association that formed and then transformed itself during a distinctive period of American history. Socially, American Catholics were primed to emerge from what they called their “ghetto,” as this formerly largely immigrant group began to enter the economic and social mainstream. Institutions of higher education and psychology were recipients, moreover, of federal funding in the wake of World War II, and some of this money flowed to Catholic institutions. The ACPA began in 1947 as a way to bring Catholics in greater numbers into psychology and also to bring a Catholic perspective to bear on psychology. This article describes and analyzes the major initiatives of the ACPA: the establishment of undergraduate and graduate programs in psychology at Catholic colleges; placement services for members; and the development of psychological assessment programs for candidates for the religious life. In these ways, empirical psychological categories became part of Catholic discourses about psychological life. The ACPA voluntarily restructured itself in the late 1960s in response to a changed social environment and to a consensus that its first aim had been achieved. New issues were surfacing, making a denominational group irrelevant. The ACPA became Psychologists Interested in Religious Issues (PIRI) in 1970. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种基于关联规则的Web日志数据挖掘的实现方法

介绍一个基于关联规则数据挖掘Web日志分析的实现方法,提出通过对Apriori算法的改进,提高其挖掘效率,根据其结果,达到理解用户行为和改善Web结构的目的.     

关联规则理论研究及其在教学中应用

简要介绍了数据挖掘技术，详细分析了关联规则挖掘算法。分析和指出了现有一些关联规则算法的优缺点。对教学信息库进行数据挖掘．寻找高校教学体制中各因素间的并联关系．为高校管理决策提供了科学依据。     

2006 Award Winners: APA/APAGS Award for Distinguished Graduate Student in Professional Psychology.

The APA/APAGS Award for Distinguished Graduate Student in Professional Psychology is awarded on an annual basis by the Board of Professional Affairs (BPA) and the American Psychological Association of Graduate Students (APAGS) to a graduate student who has demonstrated outstanding practice and application of psychology. A qualified candidate must demonstrate exemplary performance in working with an underserved population in an applied setting or have developed an innovative method for delivering health services to an underserved population. This document lists the winners of this award from 2002 to 2006, and provides the citation, brief profile, and selected bibliography for the 2006 recipient, Mona M. Amer. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

News and notes.

This section of News & Notes provides the latest information about the Society for the History of Psychology. It contains Division news for the Division 26 of the American Psychological Association Society for the History of Psychology, meeting reports for the Society for the History of Psychology Program at the Convention of the Association for Psychological Science, call for papers, general member news, and reports from the field. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

关联规则数据挖掘与发展趋势研究

论文首先简要地介绍关联规则的概念、基本原理及分类。然后详细地讨论了Apriori算法的基本原理,同时也指出了Apriori算法的一些缺陷。针对这些缺陷提出了解决方法,列举了几种改进算法。最后概述了关联规则数据挖掘的发展趋势。     

基于分布式数据库的挖掘模型

给出了一种基于分布式数据库的挖掘模型及其相应的一种有效的挖掘算法,该模型和算法在减轻网络频繁的通讯负担、体现并行计算以及异构数据挖掘方面有其独特的优点.     

A novel manufacturing defect detection method using association rule mining techniques

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.     

A high-performance distributed algorithm for mining association rules

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speed-up with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm. As a result of this tighter bound and by utilizing the combined memory of several computers, the algorithm generates far fewer candidates than comparable sequential algorithms—the same order of magnitude as the optimum.     

Application Study on Visible Human Computer Interaction System of Association Rule

With COM, VB and VC, we develop a visual human computer interaction system, which is used to mine association rules. It can mine association rules from the database which is created by Access and SQL server, as well as the text mode. With the interaction interface, user participates in the process of data mining, making the system mine the satisfying rules.