By Tomasz Sitarek, Wladyslaw Homenda (auth.), Ngoc-Thanh Nguyen, Kiem Hoang, Piotr Jȩdrzejowicz (eds.)
The volumes set LNCS 7653 and 7654 constitutes the refereed complaints of the 4th overseas convention on Computational Collective Intelligence, ICCCI, held in Ho Chi Minh urban, Vietnam, in November 2012.
The 113 revised complete papers awarded have been rigorously reviewed and chosen from 397 submissions. The papers are geared up in topical sections on (Part I) wisdom integration; facts mining for collective processing; fuzzy, modal, and collective structures; nature encouraged platforms; language processing platforms; social networks and semantic internet; agent and multi-agent platforms; category and clustering equipment; modeling and optimization innovations for enterprise intelligence; (Part II) multi-dimensional facts processing; internet platforms; clever choice making; tools for scheduling; collective intelligence in net structures – net platforms research; complex facts mining options and purposes; cooperative challenge fixing; computational swarm intelligence; and semantic equipment for wisdom discovery and communication
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Extra resources for Computational Collective Intelligence. Technologies and Applications: 4th International Conference, ICCCI 2012, Ho Chi Minh City, Vietnam, November 28-30, 2012, Proceedings, Part II
Extracting voice lines, selecting, searching. The general attempts to performing structured data operations would be applied to other domains as well. The most interesting application domain is natural language processing, since natural languages can be used to describe structures of data in diﬀerent domains including spaces of music information. Acknowledgement. This work is supported by The National Center for Research and Development, Grant no. N R02 0019 06/2009. Tomasz Sitarek contribution is supported by the Foundation for Polish Science under International PhD Projects in Intelligent Computing.
By understanding the customers’ behavior, it can help the management to design promotional strategies, determine potential buyers, increase profit-sales etc. This type of pattern is also known as frequent patterns. Apriori  was the first algorithm to capture sets of frequently bought products at stores. In AR, a set of item is defined as an itemset. The itemset is said to be frequent, if it occurs more than a predefined minimum support. In addition, confidence is another alternative measurement that always used in pair with support threshold.
Items supports for the existing prefix path may or may not update and it is based on the characteristic of a new prefix path. The final sub-process is Construct FP-Tree. This process will continue until entire prefix paths in DOSTrieIT are transformed into FP-Tree. FP-Tree data structure will be transformed into flat-file. (v) FP-Growth*: The fourth component is to mine the complete set of frequent patterns from FP-Tree* by patterns fragment-growth and without using candidate itemsets generations.
Computational Collective Intelligence. Technologies and Applications: 4th International Conference, ICCCI 2012, Ho Chi Minh City, Vietnam, November 28-30, 2012, Proceedings, Part II by Tomasz Sitarek, Wladyslaw Homenda (auth.), Ngoc-Thanh Nguyen, Kiem Hoang, Piotr Jȩdrzejowicz (eds.)