所謂資料採礦是指『從大量資料或大型資料庫中由電腦自動選取一些重要的、潛在有用的資料類型或知識』。目前資料採礦所包含的各種技術已被廣泛的應用在許多領域上，本研究即要利用資料採礦的技術從大量的客戶交易資料中採掘出客戶與商品之間的關聯性知識，並將之應用未來客戶銷售活動。 資料採礦於流通業多為B2C之應用，本研究則嘗試將資料採礦分析應用於B2B之交易分析，並以個案公司與其客戶之實際銷售資料為本研究之資料來源，本研究利用Clementine電腦軟體為資料採礦工具，並依分析目的之不同，運用該軟體提供之各項採礦模組分別對個案公司之交易資料進行分析，如： *.使用關聯網〈web〉的方式，針對個案資料，尋找商品銷售間的強弱關係，挑出銷售關聯性較高的商品組合，並且利用C5.0決策樹演算法，尋找該交易行為的對象之特性為何。 *.使用Apriori演算法，針對BZ(商圈)、DL(經銷商)、SP(門市)等不同客戶類型在不同的資料期間，找出資料中所有商品之關聯規則。 *.利用Apriori演算法，利用前半年資料，找出IFAKMB(主機板)、IFDDLC(LCD監視器)、IFCOCP(中央處理器)等類別商品的購買規則，並分別以後半年的資料進行驗證，探究此規則之可行性。 接著針對各項資料採礦結果，就個案公司之實際狀況進行解讀，同時更重要的是探討該分析結果應用於銷售實務上之可行性，如：產品銷售規則，行銷策略、促銷戰術之擬定等。最後並以本研究之結果及經驗，對個案公司提出資訊管理系統資料補強之建議及資料採礦於未來可再延伸探討之應用方向。

在現今資訊潮流中，企業的龐大資料庫可藉由統計及人工智慧的科學技術尋找出有價值的隱藏事件。利用資料做深入分析，找出其中的知識，並根據企業的問題，建立不同的模型，進而提供企業進行決策時的參考依據。資料挖掘的工作是近年來資料庫應用領域中相當熱門的議題。它雖是個神奇又時髦的技術，卻不是一門創新的學問。美國政府在第二次世界大戰前，就於人口普查以及軍事方面使用資料挖掘的分析方法。隨著資訊科技的進展，新工具的出現，以及網路通訊技術的發展，常常能超越歸納範圍的關係來執行資料挖掘，而由資料堆中挖掘寶藏，使資料挖掘成為企業智慧的一部份。在本篇論文當中，將資料挖掘技術中的關聯法則 ( Association Rule ) 運用至房地產的價格分析，進而提供有效的關聯法則，對於複雜之房價與週邊環境因素作一整合探討。購屋者將有一適當依循的投資計畫，房產業者亦可針對適當的族群做出適當的銷售企畫。 / At this technological stream of time, it is able to extract the value of corporations’ large data sets by applying the knowledge of statistics and the scientific techniques from artificial intelligence. Through the use of these algorithms, the database will be analyzed and its knowledge will be generated. In addition to these, data models will be sorted by different corporation issues resulting in the reference for any strategic decision processes. More advantages are the predictions of future events and how much public is willing to contribute and feedback to new products or promotions. The probability of outcomes will be helpful as references since this information is referable to ensure companies providing quality services at the right time. In another words, companies will have clues in attempts to understand and familiarize their customers’ needs, wants and behaviors, as a result of delivering best services for customers’ satisfactions. Data mining is such a new knowledge that is commonly discussed in the field of database applications. Although it is a relatively new term, the technology is not exactly due to the analysis methods used. Before World War II, the analysis techniques were used in particular to the statistics in census or cases related to military affairs by the US government. Knowledge discovery has been one part of business intelligence in current corporations because these new techniques are inherently geared towards explicit information, rather than just simple analysis. By applying association rules from knowledge discovery technology, this dissertation will provide a discussion of price estimation in real estates. This discussion is involved in investigations into diverse housing prices resulting from the factors of surrounding environment. By referring to this association rule, buyers will acquire information about investment plans while housing agents will gain knowledge for their plans or projects in particular to their target markets.

We study nonlinear evolutionary partial differential equations that can be viewed as a generalization of the heat equation where the temperature gradient is bounded but the heat flux is apriori only a measure. We consider this system in spatially periodic setting and use higher differentiability techniques to prove the existence and uniqueness of weak solution with integrable heat-flux for all values of the material parameter a. Under some more restrictive assumptions on a, we prove higher integrability of the heat flux. 1

This project is concerned with problems of knowledge discovery in databases, in the concrete then is concerned with an association rules, which are part of the system of data mining. By that way we try to get knowledge which we can´t find directly in the database and which can be useful. There is the description of SQL/MM DM, especially then all user-defined types given by standard for association rules as well as common types which create framework for data mining. Before the description of implementation these types, there is mentioned the instruments which are used for that - programming language PL/SQL and Oracle Data Mining support. The accuracy of implementation is verified by a sample application. In the conclusion, achieved results are evaluated and possible continuation of this work is mentioned.

This MSc Thesis handles with so-called data mining. Data mining is about obtaining some data or informations from databases, where these data or informations are not directly visible, but they are accessible by using special algorithms. This MSc Thesis mainly aims documents clasifying by selected method in scope of digital library. The selected method is based on sets of items called "itemsets method". This method extends Apriori algorithm application field originally designed for transaction databases processing and generation of sets of frequented items.

The main goal of this thesis work was to develop, implement and evaluate an algorithm that enables mining association rules from datasets that contain quantified distance information among the items. This was accomplished by extending and enhancing the Apriori Algorithm, which is the standard algorithm to mine association rules. The Apriori algorithm is not able to mine association rules that contain distance information among the items that construct the rules. This thesis enhances the main Apriori property by requiring itemsets forming rules to“deviate properly" in addition to satisfying the minimal support threshold. We say that an itemset deviates properly if all combinations of pair-wise distances among the items are highly conserved in the dataset instances where these items occur. This thesis introduces the notion of proper deviation and provides the precise procedure and measures that characterize it. Integrating the notion of distance preserving frequent itemset and proper deviation into the standard Apriori algorithm leads to the construction of our Distance-Based Association Rule Mining (DARM) algorithm. DARM can be applied in data mining and knowledge discovery from genetic, financial, retail, time sequence data, or any domain where the distance information between items is of importance. This thesis chose the area of gene expression and regulation in eukaryotic organisms as the application domain. The data from the domain was used to produce DARM rules. Sets of those rules were used for building predictive models. The accuracy of those models was tested. In addition, predictive accuracies of the models built with and without distance information were compared.

This thesis examines the problem of identifying patterns in process event logs that are correlated with binary events that are undetected until the end of the process. Specifically, we consider the task of identifying patterns in a machine shop manufacturing process that are correlated with product defect. We introduce a pattern mining algorithm based on Apriori to identify frequent patterns, and use binary correlation measures to identify patterns associated with elevated defect rate. We design a simulation model to generate synthetic datasets to test our algorithm. We compare the effectiveness of different correlation measures, target pattern complexities, and sample sizes with and without knowledge of the underlying process. We show that knowledge of the underlying process helps in identifying the pattern that is associated with defects. We also develop a decision support tool based on p-value simulation to help managers identify sources of error in real-life settings. Finally, we apply our method to real world data and extract useful information from the data to help plant managers make decisions related to investments and workforce planning. This thesis also explores the problem of predicting the defect probability given an ordered list of events and its defect status. We develop a supervised learning model using the frequency of patterns deduced from the event log as the feature set. We discuss the challenges faced in this approach and conclude that random forest algorithm performs better than other methods. We apply this approach to a real world case study and discuss the applications in the machine shop. Finally, the thesis explores the order-bidding process in the machine shop industry, and proposes an optimization-based model to maximize the profit of the machine shop. Through a case study example, we show the advantages of using the defect probability in the proposed optimization model to determine the machine-worker schedule to execute job orders in a machine shop.

Quality has become a central concern as it has been observed that reducing defects will lower the cost of production. Hence, companies generate and store vast amounts of quality related data. Analysis of this data is critical in order to understand the quality problems and their causes, and to take preventive actions. In this thesis, we propose a methodology for this analysis based on one of the data mining techniques, association rules. The methodology is applied for quality related data of an electronics company. Apriori algorithm used in this application generates an excessively large number of rules most of which are redundant. Therefore we implement a three phase elimination process on the generated rules to come up with a reasonably small set of interesting rules. The approach is applied for two different data sets of the company, one for production defects and one for raw material non-conformities. We then validate the resultant rules using a test data set for each problem type and analyze the final set of rules.

Especially with the wide use of dynamic page generation, and richer user interaction in Web, traditional web usage mining methods, which are based on the pageview concept are of limited usability. For overcoming the difficulty of capturing usage behaviour, we define the concept of semantic events. Conceptually, events are higher level actions of a user in a web site, that are technically independent of pageviews. Events are modelled as objects in the domain of the web site, with associated properties. A sample event from a video web site is the &#039 / play video event&#039 / with properties &#039 / video&#039 / , &#039 / length of video&#039 / , &#039 / name of video&#039 / , etc. When the event objects belong to the domain model of the web site&#039 / s ontology, they are referred as semantic events. In this work, we propose a new algorithm and associated framework for mining patterns of semantic events from the usage logs. We present a method for tracking and logging domain-level events of a web site, adding semantic information to events, an ordering of events in respect to the genericity of the event, and an algorithm for computing sequences of frequent events.

Frequent itemset mining, the task of finding sets of items that frequently occur to- gether in a dataset, has been at the core of the field of data mining for the past sixteen years. In that time, the size of datasets has grown much faster than has the ability of existing algorithms to handle those datasets. Consequentely, improvements are needed. In this thesis, we take the classic algorithm for the problem, A Priori, and improve it quite significantly by introducing what we call a vertical sort. We then use the benchmark large dataset, webdocs, from the FIMI 2004 conference to contrast our performance against several state-of-the-art implementations and demonstrate not only equal efficiency with lower memory usage at all support thresholds, but also the ability to mine support thresholds as yet unattempted in literature. We also indicate how we believe this work can be extended to achieve yet more impressive results.