Sentence examples for frequent items in from inspiring English sources

We apply our algorithm to the detection of frequent items in both real and synthetic datasets whose probability distribution functions are a Hurwitz and a Zipf distribution respectively.

In this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient and highperformance solution for finding frequent items in data streams.

Identifying frequent items in high-speed network is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial of service attacks.

The algorithm only searches for local frequent items in a projected database that stores potential local inter-transaction items and partitions the database into a set of smaller databases recursively.

Time complexity to store singleton frequent items in Bloom filter = f.

For 4th iteration again all use same candidate set approach due to less number of frequent items in 3rd iteration (Fig. 6b).

The algorithm determines in parallel frequent items, i.e., those whose frequency is greater than a given threshold, and is therefore useful for iceberg queries and many other different contexts.

This can be achieved by bringing each cipher item to the same level of support, e.g., to the support of the most frequent item in D. This option would lead to a dramatic explosion of the frequent patterns making pattern mining at the server side computationally prohibitive.

The most frequent item in the baseline measurement was 'answers a question' (n=3094), representing 13% of all observed verbal behaviours.

The most frequent item in the follow-up measurement was 'reacts to suggestions from others' (n=2393), representing 11.5% of all observed verbal behaviours.

In contrast, the most frequent items included in PT tools (86%, 6/7) were: baseline comparability, blinding of investigator/assessor, and use of intention-to-treat analysis.