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Learning about Grover’s Algorithm: Quantum Database Search.
- Grover, L. K. (1996). A fast quantum mechanical algorithm for database search.
- Grover, L. K. (1997). Quantum Mechanics helps in searching for a needle in a haystack Phys. Rev. Lett. 79 (1997) 325.
- Lavor, C., Manssur, L.R.U., and Portugal, R. (2008). Grover’s Algorithm: Quantum Database Search.
- Wikipedia. Grover’s algorithm.
Learn about the power of X Searches (short for XOR and XNOR searches) for keyword discovery, disambiguation, clustering, information retrieval, and data mining in general.
This is a follow up on the Beauty of XOR and XNOR searches post, describing possible applications of these search modes to Information Retrieval, Search Marketing, and Web Mining. The post is a snippet taken from http://www.minerazzi.com/help/xor-xnor.php
An IR researcher can test the performance of an LSI algorithm with a sample of documents retrieved through XOR and XNOR searches. Said sample should be rich in co-occurrence cases. Using a similar procedure, search marketers or Web intelligence specialists can identify sets of documents that emphasize keywords somehow related through different co-occurrence paths.
An interesting application consists in extracting all the unique terms (or just the high frequency ones) from a text source and constructing an XOR query with these. We may refer to this as XORing a text source. This should help one identify a network of co-occurrence paths over a collection and which documents might be relevant to specific combination of terms from the original source.
The text source can be a title, description, abstract, or paragraph of a document, or even an entire document. However, XORing a large document might be computer-intensive.
A similar exercise can be done by XNORing a text source. In both cases, the resultant output can be used to identify prospective competitors; i.e., documents relevant to similar concepts or belonging to companies within the same business space.
We are currently testing the XOR and XNOR search modes as a query disambiguation strategy.
PS. Today, 1-9-2014, we added new material that discusses these search modes for disambiguation and clustering.
Who said that IR and LSI cannot be fun? Detecting Cyberbullying: Query Terms and Techniques
A lot of SEOs regurgitate these terms across the Web with ‘correlation is not causation’, or ‘co-occurrence is this or that…’ and the like. When it comes to explaining their data, they simply mistake all those concepts.
Well… some questions for them:
Correlation is not causation: So, how do you determine and measure causality?
The answer is here: Using Statistics to Determine Causal Relationships
Co-occurrence and association: One is affected by size. Which one?
That’s the beta test phase we are in at Minerazzi (http://www.minerazzi.com). This time we are testing some nice tools.
Beta test phase 3 of Minerazzi (http://www.minerazzi.com) is now open. A new search interface with 16 search modes and few new tools will be tested. We have added a new tool that allows users to generate matrices from almost any type of analytics. Immediate applications to search result pages (serps) like in the form of keyword matrices, search mode count matrices, etc are possible.
The beauty of Venn Diagrams is in “The Search for Simple Symmetric Venn Diagrams” by Frank Ruskey, Carla D. Savage, and Stan Wagon
Stay ahead here, with a new search experience: