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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.

What is it about academia anyway? We profess to hate it, spend endless amounts of time complaining about it, and yet we in academia will do practically anything to stay. The pay may be low, job security elusive, and in the end, it's not the glamorous work we envisioned it would be. Yet, it still holds fascination and interest for us. This is an article about American academic fiction. By academic fiction, I mean novels whosemain characters are professors, college students, and those individuals associated with academia. These works reveal many truths about the higher education experience not readily available elsewhere. We learn about ourselves and the university community in which we work.

Attempts to provide employers with a procedural sequence that will reduce their liability in regard to employee sexual harassment claims. Invites employers to include this framework within their employee handbook. Concludes that it is vital employers understand what constitutes harassment and uses case law to provide short examples. Provides arguments advocating the use of the above policy.

Combining trends in employment flexibility, organizational learning, need for improved leadership and entrepreneurship is important in managing today’s organizations. This study aims to explore these relationships within a single media firm in one of the Baltic States.

The subject for this exploratory case study is a small Estonian media company having a total of 43 members/employees. Data for the study were collected using two questionnaires (organizational leadership capability and dimensions of learning organizations) and by in-depth interviews. Assessment and analysis of the data included: measurement of organizational leadership (OL) and learning organization; measurement of entrepreneurial behavior; and analysis of the results gained from studying the issues pertaining to OL, learning organization and entrepreneurial behavior.

The results of this study reveal that part-time versus full-time employees have more positive attitudes toward the organization’s decentralized leadership and of six of seven learning characteristics. It appears that the entrepreneurial orientation of the part-time employees (PTEs) helps explain the differences observed.

The implications for practice based on this study is that firms should consider their PTEs as a valuable asset not only because of the flexibility they offer to the workforce but also because of the special skills and outlooks they bring to the organization.

This paper explores the relationships among organizational learning, OL and entrepreneurship in context of part-time employment.