Mining the optimal class association rule set
We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.

title: (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
abstract: Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.

title: On optimal rule discovery
abstract: In machine learning and data mining, heuristic and association rules are two dominant schemes for rule discovery. Heuristic rule discovery usually produces a small set of accurate rules, but fails to find many globally optimal rules. Association rule discovery generates all rules satisfying some constraints, but yields too many rules and is infeasible when the minimum support is small. Here, we present a unified framework for the discovery of a family of optimal rule sets and characterize the relationships with other rule-discovery schemes such as nonredundant association rule discovery. We theoretically and empirically show that optimal rule discovery is significantly more efficient than association rule discovery independent of data structure and implementation. Optimal rule discovery is an efficient alternative to association rule discovery, especially when the minimum support is low.

title: Mining risk patterns in medical data
abstract: In this paper, we discuss a problem of finding risk patterns in medical data. We define risk patterns by a statistical metric, relative risk, which has been widely used in epidemiological research. We characterise the problem of mining risk patterns as an optimal rule discovery problem. We study an anti-monotone property for mining optimal risk pattern sets and present an algorithm to make use of the property in risk pattern discovery. The method has been applied to a real world data set to find patterns associated with an allergic event for ACE inhibitors. The algorithm has generated some useful results for medical researchers.

title: Mining Optimal Class Association Rule Set
abstract: We define an optimal class association rule set to be the minimum rule set with the same prediction power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less time than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.

title: Mining the smallest association rule set for predictions
abstract: Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We present an algorithm to directly generate the informative rule set, i.e., without generating all frequent itemsets first, and that accesses the database less often than other unconstrained direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set, and that it can be generated more efficiently.

title: Discovery of Binding Motif Pairs from Protein Complex Structural Data and Protein Interaction Sequence Data
abstract: Unravelling the underlying mechanisms of protein interactions requires knowledge about the interactions' binding sites. In this paper, we use a novel concept, binding motif pairs, to describe binding sites. A binding motif pair consists of two motifs each derived from one side of the binding protein sequences. The discovery is a directed approach that uses a combination of two data sources: 3-D structures of protein complexes and sequences of interacting proteins. We first extract maximal contact segment pairs from the protein complexes' structural data. We then use these segment pairs as templates to sub-group the interacting protein sequence dataset, and conduct an iterative refinement to derive significant binding motif pairs. This combination approach is efficient in handling large datasets of protein interactions. From a dataset of 78,390 protein interactions, we have discovered 896 significant binding motif pairs. The discovered motif pairs include many novel motif pairs as well as motifs that agree well with experimentally validated patterns in the literature.

title: Text and picture segmentation by the distribution analysis of wavelet coefficients
abstract: This paper presents an algorithm to segment text and picture in an image using two features based on the statistical distribution of the wavelet coefficients in high frequency bands. The algorithm breaks the image into blocks and classifies every block as background, text or picture according to the two features. The block size is variable so that the segmentation can be accurate at the boundary of two types and avoids misclassifying due to over-localized region analysis.

title: A Rough Set Based Model to Rank the Importance of Association Rules
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based process by which a rule importance measure is calculated for association rules to select the most appropriate rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule among the rule sets. Rule importance is different from rule interestingness in that it does not consider the predefined knowledge on what kind of information is considered to be interesting. The experimental results show our method reduces the number of rules generated and at the same time provides a measure of how important is a rule.

title: The Empirical Studies on Quality Benefits of Reusing Software Components
abstract: The benefits of reusing software components have been studied for many years. Several previous studies have concluded that reused components have fewer defects in general than non-reusable components. However, few of these studies have gone a further step, i.e., investigating which type of defects has been reduced because of reuse. Thus, it is suspected that making a software component reusable will automatically improve its quality. This paper presents an on-going industrial empirical study on the quality benefits of reuse. We are going to compare the defects types, which are classified by ODC (orthogonal defect classification), of the reusable component vs. the non-reusable components in several large and medium software systems. The intention is to figure out which defects have been reduced because of reuse and the reasons of the reduction.

title: Introducing a Rule Importance Measure
abstract: Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.