The importance of having ef cient and effective methods for datamining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recen...
详细信息
ISBN:
(数字)9781441916303
ISBN:
(纸本)9781441916297;9781461426134
The importance of having ef cient and effective methods for datamining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.
This book addresses the integration of two areas of computer science, namely datamining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integrati...
详细信息
ISBN:
(数字)9783662049235
ISBN:
(纸本)9783540433316;9783642077630
This book addresses the integration of two areas of computer science, namely datamining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research. In essence, datamining consists of extracting valid, comprehensible, and in teresting knowledge from data. datamining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, datamining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog nition). Our discussion of datamining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. In a nutshell, the motivation for applying evolutionary algorithms to datamining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions (rules or another form of knowl edge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.
Modeling and forecasting are impressive and active research areas, which have been widely used in diverse theoretical and practical applications, successfully. Accuracy is the most important and at the same time is th...
详细信息
Modeling and forecasting are impressive and active research areas, which have been widely used in diverse theoretical and practical applications, successfully. Accuracy is the most important and at the same time is the most challenging feature of modeling and forecasting approaches that considerable efforts have been made in the literature for improving it. However, despite all interminable endeavors done in this regard, improving accuracy is yet often a problematic task. Weighting are among the most fundamental and critical parts of modeling and forecasting approaches, which have a significant impact on the performance and accuracy. Nevertheless, despite of the substantial importance of weighting algorithms, they have not been appropriately/comprehensively investigated in the literature. This fact that there are no such comprehensive review papers in this field is the core literature gap of modelling and forecasting that construct the main contribution of this paper. In this way, the main core objective of this paper is to comprehensively review and classify the most frequently applied weighting algorithms in various modeling and forecasting approaches. Motivated by this goal, in this review paper, different weighting algorithms, commonly used in the modelling and forecasting field, are classified based on the type of their function (i.e., Continuous/Discrete and Static/Dynamic) into four main categories. After that, the most related works done in these categorizes, including (1) discrete and static, (2) discrete and dynamic, (3) continuous and static, and (4) continuous and dynamic, are systematically reviewed to offer practical as well as theoretical guides for researchers. For this purpose, in each category, the most popular weighting algorithms, their potential limitations, and some highlighted advantages/disadvantages of them are briefly discussed. The weighting reviewed works, investigated in this paper, totally contain 350 paper that have been published sinc
This paper introduces some methodologies of data mining and knowledge discovery in databases and discusses the applications of these new intelligent methods in ocean engineering. Furthermore an integrated intelligent ...
详细信息
This paper introduces some methodologies of data mining and knowledge discovery in databases and discusses the applications of these new intelligent methods in ocean engineering. Furthermore an integrated intelligent system is presented in the paper. In the system, some artificial neural network models are applied to predict data trend and a fuzzy neural network model is used for knowledgediscovery.
Many practical methods of datamining have been developed. But theoretical basis of datamining and discovery is not yet clear. This paper locates these software technologies in a global activity on information by hum...
详细信息
ISBN:
(纸本)0780390172
Many practical methods of datamining have been developed. But theoretical basis of datamining and discovery is not yet clear. This paper locates these software technologies in a global activity on information by human and tries to make the theoretical basis of the technologies clear.
Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their abili...
详细信息
ISBN:
(数字)9783642027888
ISBN:
(纸本)9783642027871;9783642426247
Mohamed Medhat Gaber “It is not my aim to surprise or shock you – but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which the human mind has been applied” by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledgediscovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1–3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on knowledgediscovery and datamining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The t...
详细信息
ISBN:
(数字)9783642374562
ISBN:
(纸本)9783642374555
The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on knowledgediscovery and datamining, PAKDD 2013, held in Gold Coast, Australia, in April 2013.
The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of datamining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving datamining, recommendation, multimedia datamining, stream datamining, data preprocessing and representation.
暂无评论