Privacy preservation is becoming a critical issue to data‐mining processes. In practice, a data transformation process is often needed to preserve privacy. However, data transformation would introduce a data quality issue. In this case, the impact on data quality due to the data transformation should be estimated and made clear to the user of the data transformation process. In this article, we consider the problem of k ‐anonymization transformation in associative classification. The privacy preservation and data quality issues are considered in twofold. First, we propose a frequency‐based data quality metric to represent the data quality for associative classification. Second, a novel heuristic algorithm, namely minimum classification correction rate transformation, is proposed. The algorithm is guided by the classification correction rate of the given datasets. We validate our proposed metric and algorithm with University of California–Irvine repository datasets. The experiment results have shown that our proposed metric can effectively demonstrate the data quality for associative classification. The results also show that the proposed algorithm is not only efficient but also highly effective.

Automatic recognition or classification of medical image modality can provide valuable information for medical image retrieval and analysis. In this paper, we discuss an application of SVM ensemble classifiers to the problem, and explore a confidence indicator based late fusion method to resolve ambiguity across competing classes. Using a matrix of closeness and a set of additional fusion rules, the proposed method improves the classification performance by only subjecting likely misclassified samples to a text-based classifier followed by additional fusion of both image-based classification and text-based classification results. An empirical evaluation using standard ImageClef2010 Medical Retrieval data show very promising performance for the proposed approach.

Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.

Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach.

Pattern discovery in temporal event sequences is of great importance in many application domains, such as telecommunication network fault analysis. In reality, not every type of event has an accurate timestamp. Some of them, defined as inaccurate events may only have an interval as possible time of occurrence. The existence of inaccurate events may cause uncertainty in event ordering. The traditional support model cannot deal with this uncertainty, which would cause some interesting patterns to be missing. A new concept, precise support, is introduced to evaluate the probability of a pattern contained in a sequence. Based on this new metric, we define the uncertainty model and present an algorithm to discover interesting patterns in the sequence database that has one type of inaccurate event. In our model, the number of types of inaccurate events can be extended to k readily, however, at a cost of increasing computational complexity.