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Cluster analysis can be employed as a data exploration tool as well as a hypothesis testing and confirmation tool.
For instance, consider the following example: Open image in new window Fig. 1 Series of sequential queries generated in a data exploration session.
Clustering is a data exploration technique that identifies groups of objects that are similar to each other but different from objects in other groups [1].
A data exploration session generally involves multiple related queries, and overlapping between results of those queries is naturally expected to occur.
But now they've renamed themselves Argo, raised a $1.525 million seed round led by Accel Partners, and pivoted to build "a data exploration tool that connects people across departments to help them collaborate", says 21-year old co-founder Ari Dyckovsky.
To capture the performance of solutions for sequential diversification during a data exploration session, we define the following metrics: Diversification cost, (C(S_i)), is defined as the processing cost, in terms of number of distance and comparison operations, required to process a result (X_i) to produce a diversified result (S_i) of size k.
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A recent data exploration tool is GEOmetadb (15), a GEO microarray search tool that leverages the metadata associated with samples, platforms and data sets.
By Anna McCollister-Slipp Anna McCollister-Slipp is co-founder of Galileo Analytics, a visual data exploration and advanced data analytics company focused on democratizing access to and understanding of complex health data.
The computational framework and experimental approach presented here essentially represents a supervised data exploration system.
It is related to a heterogeneous data exploration topic (or heterogeneous data mining).
By reducing the number of windows and databases required for a search, data exploration may be improved by limiting the risk of user confusion.
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