東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Interactive data mining.

Zhao, Yan.

FindBook

Google Book

Amazon

博客來

Interactive data mining.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interactive data mining./
作者:	Zhao, Yan.
面頁冊數:	187 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7460.
Contained By:	Dissertation Abstracts International68-11B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR33574
ISBN:	9780494335741

Interactive data mining.
Zhao, Yan.

Interactive data mining. - 187 p.

Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7460.

Thesis (Ph.D.)--The University of Regina (Canada), 2007.

Exploring and extracting knowledge from data is one of the fundamental problems in science. Data mining applies computer technologies to important data analysis tasks, such as description, prediction and explanation of data.

ISBN: 9780494335741Subjects--Topical Terms:

626642
Computer Science.

Interactive data mining.
LDR:04168nam 2200313 a 45 001 963641
005 20110831
008 110831s2007 ||||||||||||||||| ||eng d
020 $a 9780494335741
035 $a (UMI)AAINR33574
035 $a AAINR33574
040 $a UMI $c UMI
100 1 $a Zhao, Yan. $3 1033755
245 1 0 $a Interactive data mining.
300 $a 187 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7460.
502 $a Thesis (Ph.D.)--The University of Regina (Canada), 2007.
520 $a Exploring and extracting knowledge from data is one of the fundamental problems in science. Data mining applies computer technologies to important data analysis tasks, such as description, prediction and explanation of data.
520 $a While many data mining models concentrate on automation and efficiency, we argue that interactive data mining systems, focusing adaptive and effective communication between human users and computer systems, are required for data mining tasks. Through interaction and communication, computers and users can share the tasks involved in order to achieve a balance of automation and human control. Interactive data mining can encourage users' learning, improve insight and understanding of the problem to be solved, and stimulate users to explore creative possibilities. Users' feedback can be used to improve the system. The interaction is mutually beneficial, and imposes new coordination demands on both sides.
520 $a The three-layered framework of data mining provides a basis for the study of interactive data mining. This framework consists of the philosophy layer, the technique layer and the application layer. As an essential extension, user preference and requirements are added to the three layers to form a user-oriented three-layered conceptual framework. The fundamental issues of these three layers in combination with user preferences and requirements are discussed.
520 $a At the philosophical level, we examine multiple views regarding both rule formation and rule representation. We discuss various interpretations and forms of rules, as well as various underlying object relations of rules. We also discuss diverse ways to define rules. Instead of using the entire attribute set to define rules, one can use a selected attribute set, a minimum attribute set, a set of preferred attributes, or a minimum set of preferred attributes to define rules.
520 $a At the technical level, we study search spaces, search strategies, search heuristics and evaluation criteria for rule construction. The selection of these issues influences, and is associated with, each other. For example, a certain search space requires a certain search strategy, a certain form of rules can be discovered only in a certain search space, and a certain search strategy can be executed with several search heuristics, and assisted with several pruning methods. We argue that an interactive system can support both the philosophical inquiries and the technical selections regarding these issues during a rule mining process.
520 $a At the application level, we introduce how to apply rule mining methods to construct plausible explanations of discovered patterns. Explanation-oriented data mining is an important component of interactive data mining. It enhances the understanding and effectiveness of data mining results.
520 $a As a concrete example, interactive classification rule mining is elaborated upon in detail, and is demonstrated by a prototype system, Interactive Classification System (ICS). The resulting analysis and demonstration show the main concepts of this thesis. Insight regarding data and its semantics may not successfully be achieved by a computer system alone. Users, in fact, need to interact with and utilize computer systems as research tools to browse, explore and understand data, and to search for knowledge and insight from data.
590 $a School code: 0148.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 $a The University of Regina (Canada). $3 1017617
773 0 $t Dissertation Abstracts International $g 68-11B.
790 $a 0148
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR33574