data mining concepts and techniques 2011 pdf

... subsets D1 , D3 , , Dk and tested on D2 ; and so on Unlike the holdout and random subsampling methods above, here, each sample is used the same number of times for training and once for testing ... of data tuples The bootstrap method works well with small data sets 14 e is the base of natural logarithms, that is, e = 2.718 366 Chapter Classification and Prediction M1 New data sample M2 Data ... long processing times and the intricacies of complex data 7.9 Clustering High-Dimensional Data Most clustering methods are designed for clustering low-dimensional data and encounter challenges

Ngày tải lên: 08/08/2014, 18:22

... technology in molecular biology and develops algorithms and methods to manage and analyze biological data Because DNA and protein sequences are essential biological data and exist in huge volumes as ... prefix b , c , d , e , and f , respectively This can be done by constructing the b -, c -, d -, e -, and f -projected databases and mining them respectively The projected databases as well as the ... items in em )em+1 · · · en 506 Chapter Mining Stream, Time-Series, and Sequence Data Table 8.2 Projected databases and sequential patterns prefix projected database sequential patterns a (abc)(ac)d(c

Ngày tải lên: 08/08/2014, 18:22

... retrieval and multidimensional indexing methods, should be integrated with data generalization and data mining techniques to achieve satisfactory results Techniques for mining such data are further ... component of such databases can be generalized, and how the generalized data can be used for multidimensional data analysis and data mining 10.1.1 Generalization of Structured Data An important ... object-relational and object-oriented databases is their capability of storing, accessing, and modeling complex structure-valued data, such as set- and list-valued data and data with nested structures

Ngày tải lên: 08/08/2014, 18:22

... Applications and Trends in Data Mining Coupling data mining with database and/ or data warehouse systems: A data mining system should be coupled with a database and/ or data warehouse system, where ... standardize data mining products and to 11.2 Data Mining System Products and Research Prototypes 663 ensure the interoperability of data mining systems Recent efforts at defining and standardizing data mining ... difficulties using the data stored in database systems and handling large data sets efficiently In data mining systems that are loosely coupled with database and data warehouse systems, the data are retrieved

Ngày tải lên: 08/08/2014, 18:22

... the benefits of data mining in terms of time and money savings and the discovery of new knowledge 11.5 Trends in Data Mining The diversity of data, data mining tasks, and data mining approaches ... content mining, Weblog mining, and data mining services on the Internet will become one of the most important and flourishing subfields in data mining Distributed data mining: Traditional data mining ... issues in data mining The development of efficient and effective data mining methods and systems, the construction of interactive and integrated data mining environments, the design of data mining

Ngày tải lên: 08/08/2014, 18:22

... MƠN HỌC KHAI THÁC DỮ LIỆU VÀ ỨNG DỤNG ĐỀ TÀI : KHAI THÁC ĐỒ THỊ DỰA TRÊN TÀI LIỆU : Data Mining: Concepts and Techniques, Jiawei Han TP.HCM – 12/2012 Tóm tắt nội dung đồ án Đồ thị biểu thị cho ... gom nhóm phân lớp liệu đồ thị khám phá chúng với phương pháp khai thác mẫu đồ thị Chương 9:Graph Mining 9.1 Khai thác đồ thị Đồ thị ngày trở nên quan trọng việc mơ hình hóa cấu trúc phức tạp (hợp ... lập mục video, thu hồi văn bản, phân tích Web nhu cầu phân tích liệu có cấu trúc ngày tăng graph mining trở thành nhiệm vụ quan trọng Ví Dụ: Mạng cộng tác tác giả Hình 1: Ví dụ ứng dụng đồ thị

Ngày tải lên: 12/11/2015, 13:20

... evolving field like data mining, it is difficult to compose “typical” exercises and even more difficult to work out “standard” answers Some of the exercises in Data Mining: Concepts and Techniques are ... image and signal processing, and spatial data analysis (c) Explain how the evolution of database technology led to data mining Database technology began with the development of data collection and ... from databases, statistics, and machine learning? No Data mining is more than a simple transformation of technology developed from databases, statistics, and machine learning Instead, data mining

Ngày tải lên: 16/10/2021, 15:40

... Dawn E Holmes and Lakhmi C Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms, 2012 ISBN 978-3-642-23240-4 Dawn E Holmes and Lakhmi C Jain (Eds.) Data Mining: Foundations and Intelligent ... 978-3-642-19620-1 Vol 23 Dawn E Holmes and Lakhmi C Jain (Eds.) Data Mining: Foundations and Intelligent Paradigms, 2012 ISBN 978-3-642-23165-0 Vol 12 Florin Gorunescu Data Mining, 2011 ISBN 978-3-642-19720-8 ... for Information Search and Management, 2011 ISBN 978-3-642-17915-0 Vol Menahem Friedman and Abraham Kandel Calculus Light, 2011 ISBN 978-3-642-17847-4 Vol 10 Andreas Tolk and Lakhmi C Jain Intelligence-Based

Ngày tải lên: 05/11/2019, 14:31

... E-Learning Concepts and Techniques E-Learning Concepts and Techniques E-Learning Concepts and Techniques is a collaborative e-book project by Bloomsburg ... E-Learning Concepts and Techniques online class as well as several guest authors and contributors from around the world In order to properly introduce the E-Learning Concepts and Techniques e-book, ... Technology and Consumer Electronics which are often enacted into standards by the International Chapter 11 – Web Standards 167 E-Learning Concepts and Techniques Organization of Standardization

Ngày tải lên: 11/04/2017, 08:57

... Business Case Evaluation Data Identification Data Acquisition and Filtering Data Extraction Data Validation and Cleansing Data Aggregation and Representation Data Analysis Data Visualization Utilization ... Big Data Analytics Lifecycle Business Case Evaluation Data Identification Data Acquisition and Filtering Data Extraction Data Validation and Cleansing Data Aggregation and Representation Data ... II: STORING AND ANALYZING BIG DATA CHAPTER 5: Big Data Storage Concepts CHAPTER 6: Big Data Processing Concepts CHAPTER 7: Big Data Storage Technology CHAPTER 8: Big Data Analysis Techniques APPENDIX

Ngày tải lên: 04/03/2019, 10:04

... more and more people to use R to data mining work in their research and applications This chapter introduces basic concepts and techniques for data mining, including a data mining process and ... popular data mining techniques It also presents R and its packages, functions, and task views for data mining At last, some datasets used in this book are described 1.1 Data Mining Data mining ... analysis, and text mining, and also some new techniques such as social network analysis and sentiment analysis Detailed introduction of data mining techniques can be found in text books on data mining

Ngày tải lên: 23/10/2019, 15:32

... more and more people to use R to data mining work in their research and applications This chapter introduces basic concepts and techniques for data mining, including a data mining process and ... popular data mining techniques It also presents R and its packages, functions, and task views for data mining At last, some datasets used in this book are described 1.1 Data Mining Data mining ... analysis, and text mining, and also some new techniques such as social network analysis and sentiment analysis Detailed introduction of data mining techniques can be found in text books on data mining

Ngày tải lên: 05/11/2019, 14:26

... Purposes, Intents and Limitations of Data Mining 15 Database, Data Warehouse, Data Mart, Data Set…? 15 Types of Data 19 A Note about Privacy and Security ... Data Mining Process Data Mining and You .11 Chapter Two: Organizational Understanding and Data Understanding 13 Context and Perspective 13 Learning ... Hands on Exercise 29 Preparing RapidMiner, Importing Data, and 30 Handling Missing Data 30 Data Reduction 46 Handling Inconsistent Data

Ngày tải lên: 03/04/2021, 11:35

... Purposes, Intents and Limitations of Data Mining 15 Database, Data Warehouse, Data Mart, Data Set…? 15 Types of Data 19 A Note about Privacy and Security ... Data Mining Process Data Mining and You .11 Chapter Two: Organizational Understanding and Data Understanding 13 Context and Perspective 13 Learning ... Hands on Exercise 29 Preparing RapidMiner, Importing Data, and 30 Handling Missing Data 30 Data Reduction 46 Handling Inconsistent Data

Ngày tải lên: 07/04/2021, 14:19

... Foundations of Data Mining 665 11.3.2 Statistical Data Mining 666 11.3.3 Visual and Audio Data Mining 667 11.3.4 Data Mining and Collaborative Filtering 670 11.4 Social Impacts of Data Mining 675 11.4.1 ... object-relational databases and specific application-oriented databases, such as spatial databases, time-series databases, text databases, and multimedia databases. The challenges and techniques of mining ... Reference Data in Enterprise Databases: Binding Corporate Data to the Wider World Malcolm Chisholm Data Mining: Concepts and Techniques Jiawei Han and Micheline Kamber Understanding SQL and Java...

Ngày tải lên: 08/08/2014, 18:22

... 97 2.7 Summary Data preprocessing is an important issue for both data warehousing and data mining, as real-world data tend to be incomplete, noisy, and inconsistent. Data preprocessing includes data cleaning, ... approximation of the original data. PCA is computationally inexpensive, can be applied to ordered and unordered attributes, and can handle sparse data and skewed data. Multidimensional data of more than ... 2.3 Data Cleaning 65 2.3.3 Data Cleaning as a Process Missing values, noise, and inconsistencies contribute to inaccurate data. So far, we have looked at techniques for handling missing data and...

Ngày tải lên: 08/08/2014, 18:22

... processing, and data mining. We also introduce on-line analytical mining (OLAM), a powerful paradigm that integrates OLAP with data mining technology. 3.5.1 Data Warehouse Usage Data warehouses and data ... Warehouse and OLAP Technology: An Overview 3.5 From Data Warehousing to Data Mining “How do data warehousing and OLAP relate to data mining? ” In this section, we study the usage of data warehousing ... Chapter 3 Data Warehouse and OLAP Technology: An Overview data by OLAP operations), and data mining (which supports knowledge discovery). OLAP-based data mining is referred to as OLAP mining, or...

Ngày tải lên: 08/08/2014, 18:22

... include data cube–based data aggregation and attribute- oriented induction. From a data analysis point of view, data generalization is a form of descriptive data mining. Descriptive data mining ... mining describes data in a concise and summarative manner and presents interesting general properties of the data. This is different from predic- tive data mining, which analyzes data in order to ... way, the problem of mining frequent patterns in databases is transformed to that of mining the FP-tree. 228 Chapter 5 Mining Frequent Patterns, Associations, and Correlations databases. We begin...

Ngày tải lên: 08/08/2014, 18:22

... to as training tuples and are selected from the database under analysis. In the context of classification, data tuples can be referred to as samples, examples, instances, data points, or objects. 2 Because ... scalability. While both SLIQandSPRINThandle disk-resident data sets thatare too large to fit into memory, the scalabilityof SLIQ islimited by the useof its memory-residentdatastructure. SPRINT removes ... even for real-world data. RainForest has techniques, however, for handling the case where the AVC-group does not fit in memory. RainForest can use any attribute selection measure and was shown to...

Ngày tải lên: 08/08/2014, 18:22