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The cluster hypothesis was formulated first by van Rijsbergen: [3] "closely associated documents tend to be relevant to the same requests". Thus, theoretically, a search engine could try to locate only the appropriate cluster for a query, and then allow users to browse through this cluster. Although experiments showed that the cluster ...
The Hopkins statistic (introduced by Brian Hopkins and John Gordon Skellam) is a way of measuring the cluster tendency of a data set. [1] It belongs to the family of sparse sampling tests. It acts as a statistical hypothesis test where the null hypothesis is that the data is generated by a Poisson point process and are thus uniformly randomly ...
The cluster hypothesis, proposed by C. J. van Rijsbergen in 1979, asserts that two documents that are similar to each other have a high likelihood of being relevant to the same information need. With respect to the embedding similarity space, the cluster hypothesis can be interpreted globally or locally. [ 4 ]
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
Testing a hypothesis suggested by the data can very easily result in false positives (type I errors). If one looks long enough and in enough different places, eventually data can be found to support any hypothesis. Yet, these positive data do not by themselves constitute evidence that the hypothesis is correct. The negative test data that were ...
R, an open-source programming language for statistical computing and graphics. Together with Python one of the most popular languages for data science. TinkerPlots an EDA software for upper elementary and middle school students. Weka an open source data mining package that includes visualization and EDA tools such as targeted projection pursuit.
Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets. In contrast with other cluster analysis techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outlier points.
Model-based clustering [1] based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not ...