A new method to rank fuzzy numbers using Dempster-Shafer theory with fuzzy targets

In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster-Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the results from different viewpoints are treated as different sources of evidence, and Murphy's combination rule is used to aggregate the fuzzy ranking results. DST allows fuzzy numbers to be compared and ranked while preserving their uncertain and imprecise characteristics. In addition, a hybrid method consisting of fuzzy targets and DST with the Transferable Belief Model is formulated, which fulfils a number of important ordering properties. A series of empirical experiments with benchmark examples has been conducted and the experimental results clearly indicate the usefulness of the proposed method.

Personality differences and hotel web design study using targeted positive and negative association rule mining

As people have unique tastes, the way to satisfy a small group of targeted customers or to be generic to meet most people's preference has been a traditional question to many fashion designers and website developers. This study examined the relationship between individuals' personality differences and their web design preferences. Each individual's personality is represented by a combination of five traits, and 15 website design-related features are considered to test the users' preference. We introduced a data mining technique called targeted positive and negative association rule mining to analyze a dataset containing the survey results collected from undergraduate students. The results of this study not only suggest the importance of providing specific designs to attract individual customers, but also provide valuable input on the Big Five personality traits in their entirety.

Analyzing security protocols using association rule mining

Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.

A computational approach to the reconstruction of surface geometry from early temple superstructures

Recovering the control or implicit geometry underlying temple architecture requires bringing together fragments of evidence from field measurements, relating these to mathematical and geometric descriptions in canonical texts and proposing "best-fit" constructive models. While scholars in the field have traditionally used manual methods, the innovative application of niche computational techniques can help extend the study of artefact geometry. This paper demonstrates the application of a hybrid computational approach to the problem of recovering the surface geometry of early temple superstructures. The approach combines field measurements of temples, close-range architectural photogrammetry, rule-based generation and parametric modelling. The computing of surface geometry comprises a rule-based global model governing the overall form of the superstructure, several local models for individual motifs using photogrammetry and an intermediate geometry model that combines the two. To explain the technique and the different models, the paper examines an illustrative example of surface geometry reconstruction based on studies undertaken on a tenth century stone superstructure from western India. The example demonstrates that a combination of computational methods yields sophisticated models of the constructive geometry underlying temple form and that these digital artefacts can form the basis for in depth comparative analysis of temples, arising out of similar techniques, spread over geography, culture and time.

Depth first rule heneration for text categorization

Classification methods are usually used to categorize text documents, such as, Rocchio method, Naïve bayes based method, and SVM based text classification method. These methods learn labeled text documents and then construct classifiers. The generated classifiers can predict which category is located for a new coming text document. The keywords in the document are often used to form rules to categorize text documents, for example “kw = computer” can be a rule for the IT documents category. However, the number of keywords is very large. To select keywords from the large number of keywords is a challenging work. Recently, a rule generation method based on enumeration of all possible keywords combinations has been proposed [2]. In this method, there remains a crucial problem: how to prune irrelevant combinations at the early stages of the rule generation procedure. In this paper, we propose a method than can effectively prune irrelative keywords at an early stage.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

Positivism: one ring to rule them all? A tale of information systems research methodology

The tale of research methodology in information systems is told through the fantasy of Tolkien’s Lord of the Rings. The tale is intended to be at once a piece of light hearted fun in its placement of the struggles of research methodology as an epic story but, in the tradition of the court jester, attempts to provide a new perspective on Information Systems (IS) research methodology and our struggles with positivism in particular. Our tale is one of developing a greater maturity and confidence in IS methodology and introduces postmodern methodologies to Information Systems. Our tale, our pastiche, is itself postmodern.

Finding rule groups to classify high dimensional gene expression datasets

Microarray data provides quantitative information about the transcription profile of cells. To analyze microarray datasets, methodology of machine learning has increasingly attracted bioinformatics researchers. Some approaches of machine learning are widely used to classify and mine biological datasets. However, many gene expression datasets are extremely high dimensionality, traditional machine learning methods can not be applied effectively and efficiently. This paper proposes a robust algorithm to find out rule groups to classify gene expression datasets. Unlike the most classification algorithms, which select dimensions (genes) heuristically to form rules groups to identify classes such as cancerous and normal tissues, our algorithm guarantees finding out best-k dimensions (genes), which are most discriminative to classify samples in different classes, to form rule groups for the classification of expression datasets. Our experiments show that the rule groups obtained by our algorithm have higher accuracy than that of other classification approaches

Regulatory change, structural breaks, and transmission effects in HSIF and HSI volatility

A systematic BEKK-GARCH model with multiple switch points in the variance equations captures the structural changes that have taken place in the Hong Kong markets. Abolishment of the uptick rule in the Hong Kong stock market, increase of initial margins, and electronic trading of Hang Seng Index Futures are found to have significant impacts. These changes affect the volatility structure of the HSI and HSIF and hence their lead-lag relationship. The multivariate GARCH model with three specific switching points is found to be superior to any other combination of up to six separate switch points.

Measuring moral judgement and the implications of cooperative education and rule-based learning

The Defining Issues Test (DIT), developed by Rest (1986), measures a person's level of moral development using hypothetical social dilemmas. Although the DIT is useful for measuring moral development in social settings, it might not adequately capture an individual's moral judgement abilities in solving work-related problems (Weber, 1990; Trevino, 1992; Welton et al., 1994). In the present study, the moral judgement levels of 97 accounting students were measured over a 1 year period using two separate test instruments, the DIT and a context-specific instrument developed by Welton et al. (1994). The test scores are significantly higher on the DIT than the Welton instrument (between the instruments and over time), suggesting that accounting students use higher levels of moral reasoning in resolving hypothetical social dilemmas and lower levels of moral reasoning in resolving context-specific dilemmas. The difference in test scores was highest during cooperative education (work placement programme), implying that the environment is a significant determinant on students' test scores.

Systematic review and meta-analysis of evidence for increasing numbers of drugs in antiretroviral combination therapy

Objective: To assess the evidence for the effectiveness of increasing numbers of drugs in antiretroviral combination therapy.

Design: Systematic review, meta-analysis, and meta-regression of fully reported randomised controlled trials. All studies included compared quadruple versus triple therapy, triple versus double therapy, double versus monotherapy, or monotherapy versus placebo or no treatment.

Participants: Patients with any stage of HIV infection who had not received antiretroviral therapy.

Main outcome measures: Changes in disease progression or death (clinical outcomes); CD4 count and plasma viral load (surrogate markers).
Search strategy: Six electronic databases, including Medline, Embase, and the Cochrane Library, searched up to February 2001.

Results: 54 randomised controlled trials, most of good quality, with 66 comparison groups were included in the analysis. For both the clinical outcomes and surrogate markers, combinations with up to and including three (triple therapy) were progressively and significantly more effective. The odds ratio for disease progression or death for triple therapy compared with double therapy was 0.6 (95% confidence interval 0.5 to 0.8). Heterogeneity in effect sizes was present in many outcomes but was largely related to the drugs used and trial quality.

Conclusions: Evidence from randomised controlled trials supports the use of triple therapy. Research is needed on the effectiveness of quadruple therapies and the relative effectiveness of specific combinations of drugs.

Use of calcium or calcuim in combination with vitamin D supplementation to prevent fractures and bone loss in people aged 50 years and older: a meta-analysis

Background:  Whether calcium supplementation can reduce osteoporotic fractures is uncertain. We did a meta-analysis to include all the randomised trials in which calcium, or calcium in combination with vitamin D, was used to prevent fracture and osteoporotic bone loss.

Methods:  We identified 29 randomised trials (n=63 897) using electronic databases, supplemented by a hand-search of reference lists, review articles, and conference abstracts. All randomised trials that recruited people aged 50 years or older were eligible. The main outcomes were fractures of all types and percentage change of bone-mineral density from baseline. Data were pooled by use of a random-effect model.

Findings:  In trials that reported fracture as an outcome (17 trials, n=52 625), treatment was associated with a 12% risk reduction in fractures of all types (risk ratio 0·88, 95% CI 0·83–0·95; p=0·0004). In trials that reported bone-mineral density as an outcome (23 trials, n=41 419), the treatment was associated with a reduced rate of bone loss of 0·54% (0·35–0·73; p<0·0001) at the hip and 1·19% (0·76–1·61%; p<0·0001) in the spine. The fracture risk reduction was significantly greater (24%) in trials in which the compliance rate was high (p<0·0001). The treatment effect was better with calcium doses of 1200 mg or more than with doses less than 1200 mg (0·80 vs 0·94; p=0·006), and with vitamin D doses of 800 IU or more than with doses less than 800 IU (0·84 vs 0·87; p=0·03).

Interpretation:  Evidence supports the use of calcium, or calcium in combination with vitamin D supplementation, in the preventive treatment of osteoporosis in people aged 50 years or older. For best therapeutic effect, we recommend minimum doses of 1200 mg of calcium, and 800 IU of vitamin D (for combined calcium plus vitamin D supplementation).