Mining more gold from web page hyperlinks

The development of the Internet has boosted prosperity of the World Wide Web, which is now a huge information source. Because of characteristics of the web, in most cases, traditional databasebased technologies are no longer suitable for web information retrieval and management. To effectively manage web information, it is necessary to reveal intrinsic relationships/structures among web information objects by eliminating noise factors. This paper proposes a mechanism that could be widely used in information processing, including web information processing and noise factor elimination for getting more intrinsic relationships. As an application case of this mechanism, one relevant web page finding algorithm is proposed to uncover intrinsic relationship among web pages from their hyperlink patterns, and find more semantic relevant web pages. The experimental evaluation shows the feasibility and effectiveness of the algorithm and demonstrates the potential of the proposed mechanism in web applications.

Medical image classification using an efficient data mining technique

Data mining refers to extracting or "mining" knowledge from large amounts of data. It is an increasingly popular field that uses statistical, visualization, machine learning, and other data manipulation and knowledge extraction techniques aimed at gaining an insight into the relationships and patterns hidden in the data. Availability of digital data within picture archiving and communication systems raises a possibility of health care and research enhancement associated with manipulation, processing and handling of data by computers.That is the basis for computer-assisted radiology development. Further development of computer-assisted radiology is associated with the use of new intelligent capabilities such as multimedia support and data mining in order to discover the relevant knowledge for diagnosis. It is very useful if results of data mining can be communicated to humans in an understandable way. In this paper, we present our work on data mining in medical image archiving systems. We investigate the use of a very efficient data mining technique, a decision tree, in order to learn the knowledge for computer-assisted image analysis. We apply our method to the classification of x-ray images for lung cancer diagnosis. The proposed technique is based on an inductive decision tree learning algorithm that has low complexity with high transparency and accuracy. The results show that the proposed algorithm is robust, accurate, fast, and it produces a comprehensible structure, summarizing the knowledge it induces.

Characteristics and underpricing of Australian mining and energy IPOs from 1994 to 2001

This paper follows How (2000) who examined 130 Australian mining and energy initial public offerings (IPOs) from 1979 to 1990 to report an average 107.18 % underpricing return by those IPOs. This study updates that report by investigating 127 Australian mining and energy IPOs from 1994 to 2001 to find a substantially lower 17.93 % average first day return. These updated findings have implications for both new companies seeking to float and also for the subscribers wishing to invest in these new listings.

Stakeholder and gender characteristics of mining and energy IPO boards of directors

The mining and energy sectors are particularly publicly sensitive sectors and subject to a high degree of public scrutiny. Evan and Freeman (1993) suggest that such public scrutiny needs may be better met by having direct public stakeholder representation on the board of directors. Similarly, Bilimoria (2000) argues a strong commercial case for engaging women on boards. This paper investigates the number and proportion of non equity holding public stakeholder directors and the number and proportion of women directors on the boards of Australian mining and energy company initial public offerings (IPOs) and reports a paucity of public stakeholder directors and also a low proportional female representation on such IPO boards.

Reducing performance bias for unbalanced text mining

In text categorization applications, class imbalance, which refers to an uneven data distribution where one class is represented by far more less instances than the others, is a commonly encountered problem. In such a situation, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced, learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This paper aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one-class classifiers achieve more balanced performance than the standard approaches.

Adaptive spike detection for resilient data stream mining

Automated adversarial detection systems can fail when under attack by adversaries. As part of a resilient data stream mining system to reduce the possibility of such failure, adaptive spike detection is attribute ranking and selection without class-labels. The first part of adaptive spike detection requires weighing all attributes for spiky-ness to rank them. The second part involves filtering some attributes with extreme weights to choose the best ones for computing each example’s suspicion score. Within an identity crime detection domain, adaptive spike detection is validated on a few million real credit applications with adversarial activity. The results are F-measure curves on eleven experiments and relative weights discussion on the best experiment. The results reinforce adaptive spike detection’s effectiveness for class-label-free attribute ranking and selection.

Mining inconsistent secure messages toward analyzing security protocols

Traditional approaches such as theorem proving and model checking have been successfully used to analyze security protocols. Ideally, they assume the data communication is reliable and require the user to predetermine authentication goals. However, missing and inconsistent data have been greatly ignored, and the increasingly complicated security protocol makes it difficult to predefine such goals. This paper presents a novel approach to analyze security protocols using association rule mining. It is able to not only validate the reliability of transactions but also discover potential correlations between secure messages. The algorithm and experiment demonstrate that our approaches are useful and promising.

Agents and data mining: mutual enhancement by integration

This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.

Structural characterization of rare intramolecularly (1,4-Te· · ·N) bonded diorganotellurides and their monomeric complexes with mercury(II) halides: metal assisted C-H· · ·X (Hg) interactions leading to supramolecular architecture

Monomeric tellurides 4-RC₆H₄(SB)Te [SB = 2-(4,4'-N0₂C₆H₄CH=NC₆H₃-Me); R = H, 1a; Me,1b; OMe, 1c], which incidentally represent the first example of a telluride with 1,4-Te···N intramolecular interaction, have been prepared and characterized by solution and solid-state ¹²⁵Te NMR, ¹³C NMR and X-ray crystallography. Interplay of weak C-H···O and C-H-··π{ interactions in the crystal lattice of 1b and1c are responsible for the formation of supramolecular motifs. These tellurides undergo expected oxidative addition reactions with halogens and interhalogens and also interact coordinatively with mercury(II) halides to give 1:2 complexes, HgX₂[4-RC₆H₄(SB)Te]₂ (X = CI, R = H, 2a; Me, 2b; OMe, 2c and X = Br, R = H, 3a; Me, 3b; and OMe, 3c) with no sign of Te-C bond cleavage, as has been reported for some 1,5-Te·· ·N(O) intramolecularly bonded tellurides. The complexes 2a and 3c are the first structurally characterized monomeric 1:2 adducts of mercury(II) halides with Te ligands. The 1,4-Te···N intramolecular interactions in the solid-state are retained in the complexes highlighting simultaneously the Lewis acid and base character of the Te(lI) atom. Packing of molecules in the crystal lattice of 2a
and 3c reveals that non-covalent C-H· . ·Cl/Br interactions involving metal-bound halogen atoms possess significant directionality and in
combination with coordinative covalent interactions may be of potential use in creating inorganic supramolecular synthons.

EStream: online mining of frequent sets with precise error guarantee

In data stream applications, a good approximation obtained in a timely  manner is often better than the exact answer that’s delayed beyond the window of opportunity. Of course, the quality of the approximate is as important as its timely delivery. Unfortunately, algorithms capable of online processing do not conform strictly to a precise error guarantee. Since online processing is essential and so is the precision of the error, it is necessary that stream algorithms meet both criteria. Yet, this is not the case for mining frequent sets in data streams. We present EStream, a novel algorithm that allows online processing while producing results strictly within the error bound. Our theoretical and experimental results show that EStream is a better candidate for finding frequent sets in data streams, when both constraints need to be satisfied.

Adaptive load shedding for mining frequent patterns from data streams

Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.

Salmoninae Commensal Bacteria Show a Positive Correlation Between Antibiotic Resistance and Mercury Resistance Profiles

Bacterial isolates from natural sites with high toxic and heavy metal contamination more frequently contain determinants for resistance to antimicrobials. Natural strains were isolated from the ingesta and external slime of Salmo salar (Linnaeus, 1758) and Salvelinusjontinalis (Mitchell, 1814). Fish specimens were acquired from Casco Bay hatcheries, Casco, ME where there is no history of antibiotic use. Seventy-nine bacterial strains, including many well-documented salmonid commensals (an association from which the fish derives no benefit), were identified using 165 rRNA gene sequencing. Mercury resistant isolates were selected for initially on 25μM HgCI2. Strains were then grown at 20-24°C on Trypticase Soy Agar (TSA) plates containing 0-1000μM HgCl2 or 0-130μM Phenyl Mercuric Acetate (PMA). Mercury in the hatchery feed water due to ubiquitous non-point source deposition has selected for the mercury resistance observed in bacterial strains. Antibiotic resistance determinations, as measured by Minimum Inhibitory Concentration MIC) assays were performed on the 79 bacterial isolates using Sensititrel antimicrobial susceptibility panels. A positive linear correlation between the mercury (pMA and HgCl2) MIC's and antibiotic resistance for all observed strains was demonstrated. Conjugation experiments with Pseudomonas, Aeromonas, and Azomonas donors confirmed phenotypic transfer of penicillin and cephem resistances to Escherichia coli DH5a recipients. Conjugation experiments with Pseudomonas donors showed minimal transfer of tetracycline and minoglycoside resistances to Escherichia coli DH5a recipients. Our study suggests that the accumulation of antimicrobial resistances observed in these natural bacterial populations may be due to the indirect selective pressure exerted by environmental mercury.

Mercury content of tobacco products

Mercury contamination of food products results from contact with soil, water, and air polluted with mercury from industrial sources, as well as from the use of mercury-containing pesticides and fungicides on plants. A method was developed for extracting mercury from tobacco products. The mercury content of various tobacco products was determined, using an atomic absorption spectrophotometer.

Automatic pattern-taxonomy extraction for web mining

In this paper, we propose a model for discovering frequent sequential patterns, phrases, which can be used as profile descriptors of documents. It is indubitable that we can obtain numerous phrases using data mining algorithms. However, it is difficult to use these phrases effectively for answering what users want. Therefore, we present a pattern taxonomy extraction model which performs the task of extracting descriptive frequent sequential patterns by pruning the meaningless ones. The model then is extended and tested by applying it to the information filtering system. The results of the experiment show that pattern-based methods outperform the keyword-based methods. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system.