Knowledge Discovery fromMixed Data by Artificial Neural Network with Unsupervised Learning
AbstractKnowledge discovery or data mining from massive data is a hot issue in business and academia in recent years. Real-world data are usually of mixed-type, consisting of categorical and numeric attributes. Mining knowledge from massive, mixed data is challenge. To explore unknown data, visualized analysis allows users to gain some initial understanding regarding the data and to prepare for further analysis. Self-organizing map (SOM) has been commonly used as a visualized analysis tool due to its capability of reflecting topological order of the high-dimensional data in a low-dimensional space. Interesting patterns can thus be discovered by visual clues, possibly leading to discovery of valuable knowledge. In previous studies, an extended SOM has been proposed to visualize mixed-type data. However, the model works under the setting of supervised learning in order to measure the similarity between categorical values. In this article, we propose a model which can work under the setting of unsupervised learning so that neither class attribute nor domain expert is required. Experimental results are reported to demonstrate effectiveness of the proposed approach.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
This chapter was published in: Chung-Chian Hsu & Chien-Hao Kung , , pages 1295-1302, 2013.
This item is provided by ToKnowPress in its series Active Citizenship by Knowledge Management & Innovation: Proceedings of the Management, Knowledge and Learning International Conference 2013 with number 1295-1302.
Contact details of provider:
Web page: http://www.toknowpress.net/proceedings/978-961-6914-02-4/
information technology; knowledge discovery; self-organizingmap; visualization; unsupervised learning;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nada Trunk Širca).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.