Sentence examples for partition of items from inspiring English sources

Inspired by region partition of items, an effective hybrid algorithm based on greedy degree and expectation efficiency (GDEE) is presented in this paper.

Non-hierarchical clustering is started either from random partitioning of items into K initial clusters or an initial set point which will form clusters.

This paper uses the popular Non-hierarchical clustering method called K-mean method, which starts by random partitioning of items into K initial clusters and goes through the list of items for assigning an item to a cluster with a closest mean to an item.  .

The first problem can be corrected using a percentage-based method for the depth partitioning of items that leads to intervals containing approximately the same number of items.

If we consider only partitions of item (c) to compute stochastic bisimulations, we can have the following advantages: model reduction begins with smaller partitions, what makes computing more efficient; and the reduced model can be smaller than (b).

When those assumptions are implemented in a formal quantum model (QEMc), they predict that episodic memory will violate the additive law of probability: If memory is tested for a partition of an item's possible episodic states, the individual probabilities of remembering the item as belonging to each state must sum to more than 1.

In fact, an inspection of their proof reveals that their algorithm is O(n d3 τ Bell, where d is the maximum degree of the tree. Bell(k) denotes the kth Bell number, which is the number of unordered partitions of k items; these numbers are known to satisfy the bounds k e In k k < Bell k < k In k k [ 7].

For the practical scale, due to its intractability, we propose a segmented dynamic programming (SDP -based heuriSDP -basedheuristicons the sequence of items into a series of segments, each of which correspartitions subproblem.

Many combinatorial optimization problems include a grouping (or assignment) phase wherein a set of items are partitioned into disjoint groups or sets.

Moreover, it is hard for system developers to define the similarity of data popularity so as to partition data items into disks.

Similarly to standard bin packing, a set of items of sizes in [0,1] are to be partitioned into subsets of total size at most 1, called bins.