cluster randomized controlled trial
data clustering

Participants; sixty-five community midwives participating in a cluster randomized controlled trial to reduce baby walker use.

Improving the social and emotional climate of classrooms: A clustered randomized controlled trial testing The RULER Approach.

Funding was obtained from the Health Research Council of NZ to evaluate the interventions using a cluster randomized controlled trial, the MEWS Study.

Evaluation of a self-management patient education program for patients with chronic heart failure undergoing inpatient cardiac rehabilitation: study protocol of a cluster randomized controlled trial [2013].

Study Design: Clustered randomized controlled trial (RCT).

The study is a pair-matched cluster randomized controlled trial being conducted in eight communities in Kampala.

The closer the derived statistics are to 1 the better data clustering is.

EM is frequently used for data clustering in machine learning and computer vision.

It is similar to the k-means method in data clustering.

However, there are other methods to combat information explosion, such as web search and data clustering.

Similar but different methods are used in data clustering and data mining.

The term is used in computer science in data clustering algorithms.

Data clustering does not have a negative effect on model performance, but is rather overcome by sample-size effects.

To understand what Dirichlet processes are and the problem they solve we consider the example of data clustering.

The process is iterated on, in the same manner as general data clustering algorithms.

It is distinguished from ordinary data clustering by generating a concept description for each generated class.

Data clustering and multidimensional scaling are two such methods.

Compare the documents in the low-dimensional space (data clustering, document classification).

Data clustering can also be used to automatically discover the segments or groups within a customer data set.

Locality-sensitive hashing has much in common with data clustering and nearest neighbor search.

Such a measure can be used to compare how well different data clustering algorithms perform on a set of data.

Later, standard multivariate statistical, methods such as data clustering and principal co-ordinates analysis can be used.

Furthermore, neural networks techniques share with the fuzzy logic ones the advantage of enabling data clustering.

Isoperimetric graph partitioning for data clustering and image segmentation.

Java-based mean-shift implementation for numeric data clustering and image segmentation.

On data clustering with artificial ants.

Data clustering algorithms can be hierarchical.

This provides a theoretical foundation for using NMF for data clustering.

This paper proposes a novel, evolutionary approach to data clustering and classification which overcomes many of the limitations of traditional systems.

The BookmarkSync client software enables social bookmarking and bookmark data clustering.

The company employs textual analysis, image processing, data clustering and a variety of other methods to capture relevant content and present them as moments.