Richard S. Segall

WEB MINING: A SURVEY OF CURRENT RESEARCH, TECHNIQUES, AND SOFTWARE

The purpose of this paper is to provide a more current evaluation and update of web mining research and techniques available. Current advances in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining. For each tabulated research work, we examine such key issues as web mining process, methods/techniques, applications, data sources, and software used. Unlike previous investigators, we divide web mining processes into the following five subtasks: (1) resource finding and retrieving, (2) information selection and preprocessing, (3) patterns analysis and recognition, (4) validation and interpretation, and (5) visualization. This paper also reports the comparisons and summaries of selected software for web mining. The web mining software selected for discussion and comparison in this paper are SPSS Clementine, Megaputer PolyAnalyst, ClickTracks by web analytics, and QL2 by QL2 Software Inc. Applications of these selected web mining software to available data sets are discussed together with abundant presentations of screen shots, as well as conclusions and future directions of the research.

Review of data, text and web mining software

Purpose – 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.Design/methodology/approach – 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.Findings – 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.Research limitations/implications – The limitations are the inclusion of selected software and datasets rather than considering the entire realm of these. Thi...

Data visualization and data mining of continuous numerical and discrete nominal‐valued microarray databases for bioinformatics

Purpose – To present research in the area of the applications of modern heuristics and data mining techniques in knowledge discovery.Design/methodology/approach – 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.Findings – 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.Research limitations/implications – 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.Practical implications – The software and algorithms discussed in the ...

Some mathematical and computer modelling of neural networks

The purpose of this paper is to provide a brief background on neural networks, a summary of the mathematical models for some learning rules for neural networks, and some new computer graphics for these. The applications of these learning rules include both Boolean and continuous functions, as well as the construction of graphical mappings of the sensorary space as a two-dimensional neural grid. Numerical interpretations of the computer graphics generated for each of these learning rules are provided to serve as a guide for comparisons. The application of neural networks to solving the travelling salesman problem (TSP) is also discussed, as well as the method of simulated annealing. Computer graphics are provided for solutions to the TSP and also for activation of an output neuron for a three-layer feed-forward network which is trained using a Boolean function. Conclusions and future directions of the research are discussed.

Some quantitative methods for determining capacities and locations of military emergency medical facilities

Abstract This paper presents background on the hospital facility location model as a prelude to describing some quantitative methods for determining the optimal capacity and location of emergency medical facilities within service areas of potential occupational accidents. It is assumed that the types of services provided by each emergency medical facility in the geographical domain are either all identical or all non-identical, and the capacities of these facilities are either all equal or all unequal. The specific application in mind is to determine the optimum allocation of medical personnel and materials around a geographical location where a dangerous physical or chemical agent is expected with some probability to be dispersed.

A visual analysis of learning rule effects and variable importance for neural networks in data mining operations

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.

Mathematical modelling of inverse problems for oceans

This paper presents mathematical modelling of a theoretical “ocean” in which the fields of water velocity and turbulence and tracer concentrations are all known and together “perfectly” satisfy a steady-state advective-diffusive equation. The sensitivity to data errors is studied for box models representing portions of the ocean that are mathematically formulated as overdetermined systems. It is shown that very good results may be obtained from noisy data using naive inverse modelling techniques, provided that enough independent tracer data are available and the data are sufficiently noise-insensitive for the scales at which the model utilizes them. A formal statistical error estimate based on the Jacobian of the inverse transformation is introduced, which is useful if one has an a priori estimate of the noise level.

Web mining technologies for customer and marketing surveys

Purpose – The purpose of this paper is to illustrate the usefulness and results of applying web mining as extensions of data mining.Design/methodology/approach – 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.Findings – This paper discusses and compares the web mining technologies used by the selected software as applied to text, web, and click stream data.Research limitations/implications – 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.Practical implications – 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.Originality/va...

An update on bi-level geometric programming: A new optimization model

Abstract This short note is an addendum and corrigendum to a previous article that presented mathematical formulations for a new class of nonlinear optimization models called bi-level geometric programming (BLGP) problems. These problems are not necessarily convex and thus not solvable by standard nonlinear programming techniques. This short note corrects the optimality conditions for BLGP earlier presented by noting that these can only be used for those BLGP problems that are convex, and the optimization algorithm that is based on the method of branch-and-bound can only be used for BLGP problems that are nonconvex. Convergence criteria of the latter are discussed. The paper is further amended by presenting the mathematical formulation of the bi-level dual form for the geometric programming (GP) problem and its applications, as well as an example, and its relationship to future research directions.

Incorporating data mining and computer graphics for modeling of neural networks

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.