If companies want to stand out in the competitive market environment to raise customer awareness, they must understand customers¡¦ requirements, interests, and behaviors. In the past, a company gets customers¡¦ information to support decision making by doing market survey or analyzing historical transaction data. Some researches have showed that if a company can integrate market survey and transaction data analysis, the company can get a more comprehensive and complete information about customers. Although previous researches have indicated that it is very useful for a company to make good decisions by integrating market survey and transaction data analysis, there is still little research addressing this issue in education. The study aimed to explore the relationship between information analyzed from historical transaction data and from market survey for higher education. Our research scope focuses on the PhD programs of management schools. The result shows that student performance is strongly related to department reputation. Some suggestions for departments that want to improve their reputation as follows: improving the number of research grants gained from the National Science Council, increasing the number of graduate PhD students as supervisors and increasing the number of published papers and thesis under their supervision, expanding the number of graduate PhD students applying for National Science Council grants, and promoting the percentage of graduate PhD students teaching in public universities. It is evident that education stakeholders can combine market survey and historical data analysis to get more complete information for decision making Finally, this research would also bring up some practical suggestions and promote future research.

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Studies on data mining are being pursued in many different research areas, such as Machine Learning, Statistics, and Databases. The work presented in this thesis is based on the database perspective of data mining. The main focuses are on the temporal aspects of data mining problems, especially association rule discovery, and issues on the integration of data mining and database systems. Firstly, a theoretical framework for temporal data mining is proposed in this thesis. Within this framework, not only potential patterns but also temporal features associated with the patterns are expected to be discovered. Calendar time expressions are suggested to represent temporal features and the minimum frequency of patterns is introduced as a new threshold in the model of temporal data mining. The framework also emphasises the necessary components to support temporal data mining tasks. As a specialisation of the proposed framework, the problem of mining temporal association rules is investigated. The methodology adopted in this thesis is eventually discovering potential temporal rules by alternatively using special search techniques for various restricted problems in an interactive and iterative process. Three forms of interesting mining tasks for temporal association rules with certain constraints are identified. These tasks are the discovery of valid time periods of association rules, the discovery of periodicities of association rules, and the discovery of association rules with temporal features. The search techniques and algorithms for those individual tasks are developed and presented in this thesis. Finally, an integrated query and mining system (IQMS) is presented in this thesis, covering the description of an interactive query and mining interface (IQMI) supplied by the IQMS system, the presentation of an SQL-like temporal mining language (TML) with the ability to express various data mining tasks for temporal association rules, and the suggestion of an IQMI-based interactive data mining process. The implementation of this system demonstrates an alternative approach for the integration of the DBMS and data mining functions.

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Information retrieval is the process of fulfilling a user?s need for information by locating items in a data collection that are similar to a complex query that is often posed in natural language. Latent Semantic Indexing (LSI) was the predominant technique employed at the National Institute of Standards and Technology?s Text Retrieval Conference for many years until limitations of its scalability to large data sets were discovered. This thesis describes SCRIBE, a modification of LSI with improved scalability. SCRIBE clusters its semantic index into discrete volumes described by high-dimensional extensions to computer graphics data structures. SCRIBE?s clustering strategy limits the number of items that must be searched and provides for sub-linear time complexity in the number of documents. Experimental results with a large, natural language document collection demonstrate that SCRIBE achieves retrieval accuracy similar to LSI but requires 1/10 the time.

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No / Objectives The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size. Methods and materials This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly. Results and conclusions The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.

Enterprise decision making is continuously transforming in the wake of ever increasing amounts of data. Organizations are collecting massive amounts of data in their quest for knowledge nuggets in form of novel, interesting, understandable patterns that underlie these data. The search for knowledge is a multi-step process comprising of various phases including development of domain (business) understanding, data understanding, data preparation, modeling, evaluation and ultimately, the deployment of the discovered knowledge. These phases are represented in form of Knowledge Discovery and Data Mining (KDDM) Process Models that are meant to provide explicit support towards execution of the complex and iterative knowledge discovery process. Review of existing KDDM process models reveals that they have certain limitations (fragmented design, only a checklist-type description of tasks, lack of support towards execution of tasks, especially those of the business understanding phase etc) which are likely to affect the efficiency and effectiveness with which KDDM projects are currently carried out. This dissertation addresses the various identified limitations of existing KDDM process models through an improved model (named the Integrated Knowledge Discovery and Data Mining Process Model) which presents an integrated view of the KDDM process and provides explicit support towards execution of each one of the tasks outlined in the model. We also evaluate the effectiveness and efficiency offered by the IKDDM model against CRISP-DM, a leading KDDM process model, in aiding data mining users to execute various tasks of the KDDM process. Results of statistical tests indicate that the IKDDM model outperforms the CRISP model in terms of efficiency and effectiveness; the IKDDM model also outperforms CRISP in terms of quality of the process model itself.

Data mining at enterprise level operates on huge amount of data such as government transactions, banks, insurance companies and so on. Inevitably, these businesses produce complex data that might be distributed in nature. When mining is made on such data with a single-step, it produces business intelligence as a particular aspect. However, this is not sufficient in enterprise where different aspects and standpoints are to be considered before taking business decisions. It is required that the enterprises perform mining based on multiple features, data sources and methods. This is known as combined mining. The combined mining can produce patterns that reflect all aspects of the enterprise. Thus the derived intelligence can be used to take business decisions that lead to profits. This kind of knowledge is known as actionable knowledge. / Data mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.

<p>An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions. </p><p>This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods. </p><p> </p><p>On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.</p><p>In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.</p><p>We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,</p><p>and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy. </p><p>In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.</p> / Dissertation