Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.

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Statistical Fraud Detection: A Review

C4.5: Programs for Machine Learning (書評)

Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database and from corpus statistics. The use of a lexical database enables our method to model human common sense knowledge and the incorporation of corpus statistics allows our method to be adaptable to different domains. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows a significant correlation to human intuition

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Sentence similarity based on semantic nets and corpus statistics

Web Services power through explicit representations of Web resources underlying semantics and the development of an intelligent Web infrastructure that can fully exploit them. Semantic Web languages, such as OWL, extend RDF to let users specify ontologies comprising taxonomies of classes and inference rules. Both people and software agents can effectively use Semantic Web Services.' Agents will increasingly use the combination of semantic markup languages and Semantic Web Services to understand and autonomously manipulate Web content in significant ways. Agents will discover, communicate, and cooperate with other agents and services and-as we' 11 describe -will rely on policy-based management and control mechanisms to ensure respect for human-imposed constraints on agent interaction. Policy-based controls of Semantic Web Services can also help govern interaction with traditional (nonagent) clients.

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KAoS policy management for semantic Web services

This paper describes an application of Case-Based Reasoning to the problem of reducing the number of final-line fraud investigations in the credit approval process. The performance of a suite of algorithms, which are applied in combination to determine a diagnosis from a set of retrieved cases, is reported. An adaptive diagnosis algorithm combining several neighbourhood-based and probabilistic algorithms was found to have the best performance, and these results indicate that an adaptive solution can provide fraud filtering and case ordering functions for reducing the number of final-line fraud investigations necessary.

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Multiple algorithms for fraud detection

An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated model for text mining. Text mining discovers unseen patterns in textual databases. We've brought together the benefits of GAs for data mining and IE technology to propose a new approach for high-level knowledge discovery. Unlike previous KDT approaches, our model doesn't rely on external resources or conceptual descriptions. Instead, it performs the discovery using only information from the original corpus of text documents and from training data computed from them. The GA that produces the hypotheses is strongly guided by semantic constraints, which means that several specifically defined metrics evaluate the quality and plausibility.

Combining information extraction with genetic algorithms for text mining

Traditionally, housekeeping and tissue specific genes have been classified using direct assay of mRNA presence across different tissues, but these experiments are costly and the results not easy to compare and reproduce. In this work, a Naive Bayes classifier based only on physical and functional characteristics of genes already available in databases, like exon length and measures of chromatin compactness, has achieved a 97% success rate in classification of human housekeeping genes (93% for mouse and 90% for fruit fly). The newly obtained lists of housekeeping and tissue specific genes adhere to the expected functions and tissue expression patterns for the two classes. Overall, the classifier shows promise, and in the future additional attributes might be included to improve its discriminating power.

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Mining housekeeping genes with a Naive Bayes classifier

Inspiratory breath sounds were recorded from the chest wall during histamine challenge in five subjects with mild asthma (baseline FEV1 > 60% of predicted normal). The median frequency of the power spectrum of the breath sounds was found to correlate with the percentage change in FEV1 induced by histamine and with FEF50. The analysis suggests that for a decrease in FEV1 of 20%, the median frequency of breath sound would increase by 80 Hz. Variation in airway caliber produced a consistent alteration in the distribution of energy in inspiratory breath sound in the absence of wheeze. Spectral analysis of breath sound may be a useful addition to conventional spirometry in identifying changes in airway diameter.

Stuart Aitken

Papers

KAoS policy management for semantic Web services

Multiple algorithms for fraud detection

Combining information extraction with genetic algorithms for text mining

Mining housekeeping genes with a Naive Bayes classifier

Variation of breath sound and airway caliber induced by histamine challenge.