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Angaben zur Quelle [Bearbeiten]

Autor     Jiawei Han, Micheline Kamber
Titel    Data Mining: Concepts and Techniques (second edition)
Ort    San Francisco
Verlag    Morgan Kaufmann, Elsevier
Jahr    2006
ISBN    1-55860-901-6
URL    http://books.google.es/books?id=AfL0t-YzOrEC

Literaturverz.   

no
Fußnoten    no
Fragmente    2


Fragmente der Quelle:
[1.] Nm/Fragment 031 15 - Diskussion
Zuletzt bearbeitet: 2012-05-11 22:19:48 WiseWoman
Fragment, Gesichtet, Han Kamber 2006, Nm, SMWFragment, Schutzlevel sysop, Verschleierung

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Seite: 31, Zeilen: 15-27
Quelle: Han_Kamber_2006
Seite(n): 560, 561, 562, Zeilen: 560: 37-38; 561: 1-8; 562: 27-29
“How can we mine terrorist networks?” Traditional methods of machine learning and data mining, taking, as input, a random sample of homogeneous objects from a single relation, may not be appropriate here. The data comprising terrorist networks tend to be heterogeneous, multi-relational, and semi-structured. IDM embodies descriptive and predictive modeling. By considering links (the relationship between the objects), the more information is made available to the mining process. This brings about several new tasks.

Here we list these tasks.

(1) Group detection. Group detection is a clustering task. It predicts when sets of objects belong to the same group or cluster, based on their attributes as well as their link [structure.]

“How can we mine social networks?” Traditional methods of machine learning and data mining, taking, as input, a random sample of homogenous objects from a single

[page 561]

relation, may not be appropriate here. The data comprising social networks tend to be heterogeneous, multirelational, and semi-structured.

[...]

It embodies descriptive and predictive modeling. By considering links (the relationships between objects), more information is made available to the mining process. This brings about several new tasks. Here, we list these tasks with examples from various domains:

[page 562]

[...]

7. Group detection. Group detection is a clustering task. It predicts when a set of objects belong to the same group or cluster, based on their attributes as well as their link structure.

Anmerkungen

Taken from a textbook on data-mining without reference.

Sichter
(Hindemith), WiseWoman

[2.] Nm/Fragment 032 02 - Diskussion
Zuletzt bearbeitet: 2012-05-11 16:45:49 Graf Isolan
Fragment, Gesichtet, Han Kamber 2006, Nm, SMWFragment, Schutzlevel sysop, Verschleierung

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Quelle: Han_Kamber_2006
Seite(n): 561, 562, Zeilen: -
(2) Sub-graph detection. Subgraph identification finds characteristic subgraphs within networks. This is a form of graph search and also known as graph filtering technique.

(3) Object classification. In traditional classification methods, objects are classified on the attributes that describe them. Link-based classification predicts the category of an object-based not only on attributes, but also on links, and on the attributes of the linked objects.

[page 562]

8. Subgraph detection. Subgraph identification finds characteristic subgraphs within networks. This is a form of graph search and was described in Section 9.1.

[page 561]

1. Link-based object classification. In traditional classification methods, objects are classified based on the attributes that describe them. Link-based classification predicts the category of an object based not only on its attributes, but also on its links, and on the attributes of linked objects.

Anmerkungen

Taken from a textbook on data-mining without reference

Sichter
(Hindemith), Graf Isolan

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