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This paper seeks to present a complete set of graphical and numerical outputs of data mining performed for microarray databases of plant data as described in earlier research by the authors. A brief description of data mining is also presented, as well as a brief background of previous research.

The paper uses applications of data mining using SAS Enterprise Miner Version 4 for plant data from the Osmotic Stress Microarray Information Database (OSMID) that is available on the web for both normalized and log(2) transformed data.

This paper illustrates that useful information about the effects of environmental stress tolerances (ESTs) on plants can be obtained by using data mining.

Use of SAS Enterprise Miner was very effective for performing data mining of microarray databases with its modules of cluster analysis, decision trees, and descriptive and visual statistics.

The data used from the OSMID database are considered to be representative of those that could be used for biotech application such as the manufacture of plant‐made‐pharmaceuticals and genetically modified foods.

This paper contributes to the discussion on the use of data mining for microarray databases and specifically for studying the effects of ESTs on plants.

Provides a background on the concepts and development of data mining and data warehousing that need to be known by students and educators. Then discusses the applications of data mining for the construction of graphical mappings of the sensory space as a two‐dimensional neural network grid as well as the traveling salesman problem (TSP) and simulated annealing. Data mining is also used as a tool for the construction of computer graphics as solutions to the TSP and also for the activation of an output neuron for a three‐layer feed‐forward network that is trained using a Boolean function. Conclusions and future directions of the research are also discussed.

The purpose of this paper is to illustrate the usefulness and results of applying web mining as extensions of data mining.

Web mining is performed using three selected software to databases related to customer survey, marketing campaign data, and web site usage. The three selected software are PolyAnalyst^® of Megaputer Intelligence, Inc., SPSS Clementine^®, and ClickTracks by Web Analytics.

This paper discusses and compares the web mining technologies used by the selected software as applied to text, web, and click stream data.

The limitations include the availability of databases and software to perform the web mining. The implications include that this methodology can be extended to other databases.

The methodology used in this paper could be representative of that used for managers to manage their relationships with customers, their marketing campaigns, and their web site activities.

PolyAnalyst is applied to analyze text data of actual written hotel comments. SPSS Clementine is applied to customer web data collected in response to several different marketing campaigns, including age, gender, and income. ClickTracks is applied to click‐stream data for Bob's Fruit web site to generate click fraud report, search report with revenues, pay‐per‐click, and search keywords for all visitors.

The purpose of this paper is to review and compare selected software for data mining, text mining (TM), and web mining that are not available as free open‐source software.

Selected softwares are compared with their common and unique features. The software for data mining are SAS^® Enterprise Miner™, Megaputer PolyAnalyst^® 5.0, NeuralWare Predict^®, and BioDiscovery GeneSight^®. The software for TM are CompareSuite, SAS^® Text Miner, TextAnalyst, VisualText, Megaputer PolyAnalyst^® 5.0, and WordStat. The software for web mining are Megaputer PolyAnalyst^®, SPSS Clementine^®, ClickTracks, and QL2.

This paper discusses and compares the existing features, characteristics, and algorithms of selected software for data mining, TM, and web mining, respectively. These softwares are also applied to available data sets.

The limitations are the inclusion of selected software and datasets rather than considering the entire realm of these. This review could be used as a framework for comparing other data, text, and web mining software.

This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs.

Each of the software selected for this research has its own unique characteristics, properties, and algorithms. No other paper compares these selected softwares both visually and descriptively for all the three types of data, text, and web mining.

This study demonstrates two visual methodologies to support analysts using artificial neural networks (ANNs) in data mining operations. The first part of the paper illustrates the differences and similarities between various learning rules that might be employed by ANN data miners. Since different learning rules lead to different connection weights and stability coefficients, a graphical representation of the data that provides a novel visual means of discerning these similarities and differences is demonstrated. The second part of this research demonstrates a methodology for ANN model variable interpretation that uses network connection weights. It uses empirical marketing data to optimize an ANN and response elasticity graphs are built for each ANN model variable by plotting the derivative of the network output with respect to each variable, while changing network input in equal increments across the range of inputs for each variable. Finally, this paper concludes that such an approach to ANN model interpretation can provide data miners with a rich interpretation of variable importance.

To present research in the area of the applications of modern heuristics and data mining techniques in knowledge discovery.

Applications of data mining for neural networks using NeuralWare Predict^® software, genetic algorithms using Biodiscovery GeneSight^® (2005) software, and regression and discriminant analysis using SPSS^® were selected for bioscience data sets of continuous numerical‐valued Abalone fish data and discrete nominal‐valued mushroom data.

This paper illustrates the useful information that can be obtained using data mining for evolutionary algorithms specifically as those for neural networks, genetic algorithms, regression analysis, and discriminant analysis.

The use of NeuralWare Predict^® was a very effective method of implementing training rules for neural networks to identify the important attributes of numerical and nominal valued data.

The software and algorithms discussed in the paper can be used to visualize and mine microarray data.

The paper contributes to the discussion on the data visualization and data mining of microarray database for bioinformatics and emphasizes new applicability of modern heuristics and software.

President Bill Clinton has had many opponents and enemies, most of whom come from the political right wing. Clinton supporters contend that these opponents, throughout the Clinton presidency, systematically have sought to undermine this president with the goal of bringing down his presidency and running him out of office; and that they have sought non‐electoral means to remove him from office, including Travelgate, the death of Deputy White House Counsel Vincent Foster, the Filegate controversy, and the Monica Lewinsky matter. This bibliography identifies these and other means by presenting citations about these individuals and organizations that have opposed Clinton. The bibliography is divided into five sections: General; “The conspiracy stream of conspiracy commerce”, a White House‐produced “report” presenting its view of a right‐wing conspiracy against the Clinton presidency; Funding; Conservative organizations; and Publishing/media. Many of the annotations note the links among these key players.

On the occasion of the publication of the 20th volume of the Advances in Gender Research series, this chapter reviews the series goals and previous volumes and introduces the themes and chapters of the current one.

The chapter shows both continuity and change in approaches to theories, research methods, pedagogy, and praxis in gender studies.

Newer approaches, gender-centered, intersectional and global, offer a critique of older ways of gathering and understanding data, ways that respond to and are impacted by social change.

The chapter and the volume are intended to encourage further advances in gender research.

From one angle, abortion law appears to confirm the regime politics account of the Supreme Court; after all, the Reagan/Bush coalition succeeded in significantly curtailing the constitutional protection of abortion rights. From another angle, however, it is puzzling that the Reagan/Bush Court repeatedly refused to overturn Roe v. Wade. We argue that time and again electoral considerations led Republican elites to back away from a forceful assertion of their agenda for constitutional change. As a result, the justices generally acted within the range of possibilities acceptable to the governing regime but still typically had multiple doctrinal options from which to choose.

Paul J. Bryan is currently employed with Bromley Communications, the largest U.S. advertising agency with a focus on the Hispanic consumer. He obtained his Bachelor's degree in Sociology and Political Science from the University of Texas at San Antonio in 1996, where he studied under Richard J. Harris and Juanita M. Firestone.