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Cfi Mining in Data Streams Using Sliding Windows
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2015 |
| Abstract | Data streams are often continuous, unbounded, high-speed data and its distribution changes with time. In the recent years, data streams play a vital role in many applications. Examples of such applications include financial analysis, network monitoring, sensor networks, telecommunication data management, web usage and others. One important activity in such applications is Mining Frequent Itemsets (MiFIs). Two algorithms are described for MiFIs from data streams in the previous chapter. A main drawback observed in these algorithms is that since the number of FIs is very large, the time taken to generate them and the storage needed are very high. To overcome these drawbacks, an algorithm that mines a compact form of FIs known as Closed Frequent Itemsets (CFI) is proposed in this chapter. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://shodhganga.inflibnet.ac.in/bitstream/10603/79024/12/chapter_4.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
