Deriving Conceptual Schema from XML Databases

In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML Databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an Object-Role Model conceptual schema diagram

Who is to blame? The player or the rules of the game?

Why the performance of local value-adding firms supplying to mining MNCs in Zambia’s liberalized economy seems more constrained was the investigated problem. I conducted a multiple case study research involving 15 firms located on Copperbelt Province. Local value-adding firms are cardinal to national industrial development through their backward linkage role to mines. However, their performance is more constrained partly because of their weak internal capabilities. The real constraint for their situation lies in the “rules of the game” of supplying to mining MNCs which are externally engineered and applied by mining companies. Mining MNCs have absolute power to determine who supplies, what is supplied, what price and extent to which supplies are made whilst government acts like a spectator unlike a referee. Government’s inactivity emanates from institutional changes birthed by the 1991 economic liberalization. Addressing the firms’ situation neither lies in “doing business as usual” where mining MNCs remain more powerful nor in government fully regulating the relationship between local suppliers and mining companies. What could work is a holistic “change of the rules of the game” through government’s actively engaging stakeholders and appropriately incentivizing each category to subsequently strengthen the procurement and supplying relationship between mines and local suppliers

Development of Association Rule Mining with Efficient Positive and Negative Rules

Association rule mining (ARM) is one of the most researched areas of data mining and recently from the database community it has received much attention. In the marketing and retail communities, they are proven to be quite useful in the other more diverse fields. On this area some of the previous research is done, the concept behind association rules are provided at the beginning followed by an overview to some research. The advantages and limitations are concluded with an inference. There are several algorithms, in frequent pattern mining. The classical and most famous algorithm is Apriori. To find frequent item sets and association between different items sets is the objective of using Apriori algorithm, i.e. association rule. In this paper author considers data (Online Seller transaction data) and tries to obtain the results using weak a data mining tool. To find out best combination, association rule algorithm are used of different attributes in any data

On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

Statistical strategies for pruning all the uninteresting association rules

We propose a general framework to describe formally the problem of capturing the intensity of implication for association rules through statistical metrics. In this framework we present properties that influence the interestingness of a rule, analyze the conditions that lead a measure to perform a perfect prune at a time, and define a final proper order to sort the surviving rules. We will discuss why none of the currently employed measures can capture objective interestingness, and just the combination of some of them, in a multi-step fashion, can be reliable. In contrast, we propose a new simple modification of the Pearson coefficient that will meet all the necessary requirements. We statistically infer the convenient cut-off threshold for this new metric by empirically describing its distribution function through simulation. Final experiments serve to show the ability of our proposal.Postprint (published version

Frequent Lexicographic Algorithm for Mining Association Rules

The recent progress in computer storage technology have enable many organisations to collect and store a huge amount of data which is lead to growing demand for new techniques that can intelligently transform massive data into useful information and knowledge. The concept of data mining has brought the attention of business community in finding techniques that can extract nontrivial, implicit, previously unknown and potentially useful information from databases. Association rule mining is one of the data mining techniques which discovers strong association or correlation relationships among data. The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. The common precision measures used to complete these phases are support and confidence. Having been investigated intensively during the past few years, it has been shown that the first phase involves a major computational task. Although the second phase seems to be more straightforward, it can be costly because the size of the generated rules are normally large and in contrast only a small fraction of these rules are typically useful and important. As response to these challenges, this study is devoted towards finding faster methods for searching frequent patterns and discovery of association rules in concise form. An algorithm called Flex (Frequent lexicographic patterns) has been proposed in obtaining a good performance of searching li-equent patterns. The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. Depth first strategy and vertical counting strategy are used in mining frequent patterns and computing the support of the patterns respectively. The mined frequent patterns are then used in generating association rules. Three models were applied in this task which consist of traditional model, constraint model and representative model which produce three kinds of rules respectively; all association rules, association rules with 1-consequence and representative rules. As an additional utility in the representative model, this study proposed a set-theoretical intersection to assist users in finding duplicated rules. Four datasets from UCI machine learning repositories and domain theories except the pumsb dataset were experimented. The Flex algorithm and the other two existing algorithms Apriori and DIC under the same specification are tested toward these datasets and their extraction times for mining frequent patterns were recorded and compared. The experimental results showed that the proposed algorithm outperformed both existing algorithms especially for the case of long patterns. It also gave promising results in the case of short patterns. Two of the datasets were then chosen for further experiment on the scalability of the algorithms by increasing their size of transactions up to six times. The scale-up experiment showed that the proposed algorithm is more scalable than the other existing algorithms. The implementation of an adopted theory of representative model proved that this model is more concise than the other two models. It is shown by number of rules generated from the chosen models. Besides a small set of rules obtained, the representative model also having the lossless information and soundness properties meaning that it covers all interesting association rules and forbid derivation of weak rules. It is theoretically proven that the proposed set-theoretical intersection is able to assist users in knowing the duplication rules exist in representative model

Does it really take the state?

This paper explores the role of the state for an effective engagement of multinational corporations (MNCs) in corporate social responsibility (CSR). In the OECD context, the “shadow of hierarchy” cast by the state is considered an important incentive for MNCs to engage in CSR activities that contribute to governance. However, in areas of limited statehood, where state actors are too weak to effectively set and enforce collectively binding rules, profit-driven MNCs confront various dilemmas with respect to costly CSR standards. The lack of a credible regulatory threat by state agencies is therefore often associated with the exploitation of resources and people by MNCs, rather than with business’ social conduct. However, in this paper we argue that there are alternatives to the “shadow of hierarchy” that induce MNCs to adopt and implement CSR policies that contribute to governance in areas of limited statehood. We then discuss that in certain areas such functional equivalents still depend on some state intervention to be effective, in particular when firms are immune to reputational concerns and in complex-task areas that require the involvement of several actors in the provision of collective goods. Finally, we discuss the “dark side” of the state and show that the state can also have negative effects on the CSR engagement of MNCs. We illustrate the different ways in which statehood and the absence thereof affect CSR activities of MNCs in South Africa and conclude with some considerations on the conditions under which statehood exerts these effects.</jats:p