다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

物件導向軟體之架構(Object-Oriented Software Construction)探討

Tokyo Research Laboratory, IBM Research, 1623-14 Shimo-tsuruma, Yamato City, Kanagawa 242, JapanE-mail: ttokutrl.ibm.co.jpReceived November 13, 1996; revised April 14, 1998Given a huge database, we address the problem of finding associa-tion rules for numeric attributes, such as(Balance # I)O(CardLoan=yes),which implies that bank customers whose balances fall in a range I arelikely to use card loan with a probability greater than p. The above ruleis interesting only if the range I has some special feature with respect tothe interrelation between Balance and CardLoan. It is required that thenumber of customers whose balances are contained in I (called thesupport of I) is sufficient and also that the probability p of the conditionCardLoan=yes being met (called the confidence ratio) be muchhigher than the average probability of the condition over all the data.Our goal is to realize a system that finds such appropriate rangesautomatically. We mainly focus on computing two optimized ranges:one that maximizes the support on the condition that the confidenceratio is at least a given threshold value, and another that maximizes theconfidence ratio on the condition that the support is at least a giventhreshold number.Using techniques from computational geometry, we present novelalgorithms that compute the optimized ranges in linear time if the dataare sorted. Since sorting data with respect to each numeric attribute isexpensive in the case of huge databases that occupy much more spacethan the main memory, we instead apply randomized bucketing as thepreprocessing method and thus obtain an efficient rule-finding system.Tests show that our implementation is fast not only in theory but alsoin practice. The efficiency of our algorithm enables us to computeoptimizedrules for all combinationsof hundreds ofnumericand Booleanattributes in a reasonable time.

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Mining optimized association rules for numeric attributes

We consider the problem of finding neighboring class sets. Objects of each instance of a neighboring class set are grouped using their Euclidean distances from each other. Recently, location-based services are growing along with mobile computing infrastructure such as cellular phones and PDAs. Therefore, we expect to see the development of spatial databases that contains very large number of access records including location information. The most typical type would be a database of point objects. Records of the objects may consist of "requested service name," "number of packet transmitted" in addition to x and y coordinate values indicating where the request came from. The algorithm presented here efficiently finds sets of "service names" that were frequently close to each other in the spatial database. For example, it may find a frequent neighboring class set, where "ticket" and "timetable" are frequently requested close to each other. By recognizing this, location-based service providers can promote a "ticket" service for customers who access the "timetable."

Mining frequent neighboring class sets in spatial databases

We discuss data mining based on association rules for two numeric attributes and one Boolean attribute. For example, in a database of bank customers, Age and Balance are two numeric attributes, and CardLoan is a Boolean attribute. Taking the pair (Age, Balance) as a point in two-dimensional space, we consider an association rule of the form ((Age,Balance) ∈P)⇒(CardLoan = Yes), which implies that bank customers whose ages and balances fall within a planar region P tend to take out credit card loans with a high probability.We consider two classes of regions, rectangles and admissible (i.e., connected and x-monotone) regions. For each class, we propose efficient algorithms for computing the regions that give optimal association rules for gain, support, and confidence, respectively. We have implemented the algorithms for admissible regions as well as several advanced functions based on them in our data mining system named SONAR (System for Optimized Numeric Association Rules), where the rules are visualized by using a graphic user interface to make it easy for users to gain an intuitive understanding of rules.

Data Mining with optimized two-dimensional association rules

Mining Optimized Association Rules for Numeric Attributes

We address the problem of finding useful regions for two-dimensional association rules and decision trees. In a previous paper we presented efficient algorithms for computing optimized x-monotone regions, whose intersections with any vertical line are always undivided. In practice, however, the quality of x-monotone regions is not ideal, because the boundary of an x-monotone region tends to be notchy, and the region is likely to overfit a training dataset too much to give a good prediction for an unseen test dataset.

In this paper we instead propose the use of a rectilinear region whose intersection with any vertical line and whose intersection with any horizontal line are both undivided, so that the boundary of any rectilinear region is never notchy. This property is studied from a theoretical viewpoint. Experimental tests confirm that the rectilinear region less overfits a training database and thefore provides a better prediction for unseen test data. We also present a novel efficient algorithm for computing optimized rectilinear regions.

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Yasuhiko Morimoto

Papers

Mining optimized association rules for numeric attributes

Mining frequent neighboring class sets in spatial databases

Data Mining with optimized two-dimensional association rules

Mining Optimized Association Rules for Numeric Attributes

Computing optimized rectilinear regions for association rules