Survey On: Mining High Utility Itemsets from Transactional Database
| Autor: | Vinutha C, Yogish H.K |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
Process (engineering) InformationSystems_DATABASEMANAGEMENT Space (commercial competition) computer.software_genre Field (computer science) ComputingMethodologies_PATTERNRECOGNITION Knowledge extraction Profitability index Data mining Database transaction computer Auxiliary memory Time series database |
| Zdroj: | IOSR Journal of Computer Engineering. 16:91-93 |
| ISSN: | 2278-0661 2278-8727 |
| Popis: | Utility mining emerges as an important topic in data mining field. Mining high utility itemsets from databases refers to finding the itemsets with high profits. The meaning of itemset utility is interestingness, importance, or profitability of an item to users. Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although several relevant algorithm has been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. This research work focuses on efficient method for high utility itemset mining, to reduce number of overestimated itemsets and to reduce the search space in order to improve the performance of high utility itemset mining. Key Terms: Candidate itemset, frequent itemset, high utility itemset, utility mining, data mining. I. Introduction Now a days computers are used widely in different areas. Fast reliable and unlimited secondary storage provides a perfect environment for the users to collect and store large amount of the data. Computers are also used to extract the useful information from the mass of data. This is called as the knowledge discovery or Data mining. Data mining is the process of revealing previously unknown and potentially useful information from large databases. The primary goal is to discover the hidden patterns in the data and to extract previously unknown interesting patterns. Discovering useful patterns hidden in a database plays an essential role in several data mining tasks, like frequent pattern mining and high utility pattern mining. Among them the fundamental research topic is frequent pattern mining which can be applied to different kinds of databases such as transactional database, streaming database and time series database and many applications domains like bioinformatics, web click-stream analysis and mobile environment. |
| Databáze: | OpenAIRE |
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