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| Autor | Jiawei Han, Micheline Kamber |
| Titel | Data Mining: Concepts and Techniques (second edition) |
| Ort | San Francisco |
| Verlag | Morgan Kaufmann, Elsevier |
| Jahr | 2006 |
| Anmerkung | Chapter 9: http://www.cs.uiuc.edu/homes/hanj/cs512/bk2chaps/chapter_9.pdf |
| ISBN | 1-55860-901-6 |
| URL | http://books.google.es/books?id=AfL0t-YzOrEC |
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| Fußnoten | no |
| Fragmente | 2 |
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| How can we mine terrorist networks? Traditional methods of machine learning and data mining, taking a random sample of homogeneous objects from a single relation as input may not be appropriate. The data comprising terrorist networks [tend to be heterogeneous, multi-relational and semi-structured.] | “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. |
The source is not given. To be continued on the following page: Nm4/Fragment_340_01. |
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| [2.] Nm4/Fragment 340 01 - Diskussion Zuletzt bearbeitet: 2014-02-01 22:03:57 Hindemith | Fragment, Gesichtet, Han Kamber 2006, Nm4, SMWFragment, Schutzlevel sysop, Verschleierung |
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IDM embodies descriptive and predictive modeling. By considering links (relationship between the objects), more information is made available to the mining process. This brings about several new tasks [1]:
1. Memon Nasrullah. Investigative data mining: mathematical models for analyzing, visualizing and destabilizing terrorist networks. PhD dissertation. Aalborg University Denmark, 2007 |
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:
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. [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. [...] 8. Subgraph detection. Subgraph identification finds characteristic subgraphs within networks. This is a form of graph search and was described in Section 9.1. [...] |
The source is not given, instead the author's PhD thesis (see Nm) is quoted, which however has been published after the source. The copied text begins on the previous page: Nm4/Fragment_339_38 |
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