Wardz….

The k-means clustering algorithm [1] is a straightforward and effective algorithm for finding clusters in data. The algorithm proceeds as follows.

Step 1: Ask the user how many clusters k the data set should be partitioned into.

Step 2: Randomly assign k records to be the initial cluster center locations.

The constant boost to recall contrasts with the typical curve of forgetting we could expect with review. It would not be optimistic to expect a 400-500% boost in learning from this learning plan.
In a unique study reported in “Practical Aspects of Memory” Mangold Linton kept a diary over a four year period. She was able [...]

Next, lets examine whether using the complete-linkage criterion would result in a different clustering of this sample data set. Complete linkage seeks to minimize the distance among the records in two clusters that are farthest from each other. Figure 8.3 illustrates complete-linkage clustering for this data set.

Step 1: Since each cluster contains a single record [...]

If breaks enhance memory consolidation, there is a similar pattern that significantly enhances long term memory consolidation, and which dramatically slashes the overall time spent in learning.
From various sources that include Tony Buzans excellent book ‘Use your Head’, Peter Russell’s equally fascinating ‘The Brain Book’ and from journals of experimental and educational psychology, the following [...]

Suppose that we are interested in using single-linkage agglomerative clustering on this data set. Agglomerative methods start by assigning each record to its own cluster. Then, single linkage seeks the minimum distance between any records in two clusters. Figure 8.2 illustrates how this is accomplished for this data set. The minimum cluster distance is clearly [...]

The popular view that you need to take a break every now and then has been confirmed by French Researcher Henri Pieron. He has found that a planned series of breaks during a study period or lesson increases the probability of recall. A break every 30 minutes is probably optimum, and each break should be [...]

Clustering algorithms are either hierarchical or nonhierarchical. In hierarchical clustering, a treelike cluster structure (dendrogram) is created through recursive partitioning (divisive methods) or combining (agglomerative) of existing clusters. Agglomerative clustering methods initialize each observation to be a tiny cluster of its own. Then, in succeeding steps, the two closest clusters are aggregated into a new
combined [...]

If sleep helps you to assimilate facts, form opinions, reach solutions, and indulge in a “test run” of behaviour, then can you actually use the period of sleep to learn actively?
Experiments began in the U.S.A. in 1942 and extended to Russia in the 50’s. “Hypnopaedia” was a popular idea but there has never been any [...]

Clustering is often performed as a preliminary step in a data mining process, with the resulting clusters being used as further inputs into a different technique downstream, such as neural networks. Due to the enormous size of many present-day databases, it is often helpful to apply clustering analysis first, to reduce the search space for [...]

Clustering refers to the grouping of records, observations, or cases into classes of similar objects. A cluster is a collection of records that are similar to one another and dissimilar to records in other clusters. Clustering differs from classification in that there is no target variable for clustering. The clustering task does not try to [...]