24602 Mining the Web :Searches with Kriging, Inverse Distance Weighting, eigenVectors and Cross-Pollination 24602

Site and Features: http://www.eigensearch.com
Search engine, eigenMethod, eigenvector, mathematical, manifolds, science, technical, search tools, eigenmath, Jacobian, quantum, mechanics, manifolds,
science, physics, chemistry, law, legal, government, home, office, business, domain lookup, medical, travel, food, university students, searching, searchers, surfing, advanced search, search tools
Chemistry, mathematics, physical sciences, engineering, aerospace, astronomy, photography, news, computers, software, investment, venture capital, stakeholder, Biology, Chemistry, Geosciences, Biotechnology, Medical, Nursing, Anthropology, psychology, psychiatry, Philosophy, History, Business, bachelor, Ph.D., Masters, administrative, MBA, eigenMethod, eigenvector, mathematical, manifolds, science, technical, search tools, eigenmath, Jacobian, quantum, mechanics, manifolds, physics, chemistry, law, legal, health, government, home, office, business, domain, lookup, medical, travel, food, university, students, search, searches, search engine, directory, directories, category, categories, help, searching, searchers, surfing, advanced search, search help, search tips
Beta Users and advanced features Sign-up here... http://www.eigensearch.com/inc/constructs/betasignup.htm
Central to eigenSearch Advanced is the freedom to construct complex search explorations, save the forms for later use; and apply weight factor to each phrase and term. EigenSearch processing will apply eigenvector math and Jacobian matrices to construct search terms that are tailored to your exploration. Cross-pollination is also applied as described below. The eigenvector approach is clearly highly advanced and would normally be useful for very sophisticated applications. Nevertheless, anyone may utilize the method. An advanced form is simply a matrix in which the user types words and phrases randomly in a multi-cell form (please click thumbnail to view).
Advanced features
EigenOperator (cross pollination) and eigenvector constructs Cross document content pollination within every web site directory tree (unlike conventional search engines and tools eigenSearch checks for your terms and phrases and drills down though multiple directory documents) EigenSearch cross-pollination is applied to documents within the same (tree) level in a URL (peer documents). Thereby limiting the amount of contamination of results Illustration: "Blood Hounds" + "English Breed" will present documents that contain either of these phrases within the same peer level in a document storage structure; for example, within the directory: www.smartdogs/hounds. eigenSearch limits pollinating occurrences outside a peer level. For example; "blood hounds" + "English breed" found in two different directories would not report an eigenSearch result: i.e. "Blood hounds" found in www.smartdogs/hounds and "English Breed" found in. www.smartdogs/hounds/Europe would not be found. EigenSearch therefore searches one tree (peer) level in a site and looks for multiple occurrences of multiple phrases across all documents within this peer level. Corporate products can be tailored to drill down infinite levels for eigenOperator (cross-pollinating operator) matching. eigenSearch single phrase results will find all documents and show the results as independent findings. This way the user can find results across many documents and the combined highly constrained results are reserved for a single level cross pollination. Extremely high (cross-pollinating) eigenValues will correspond to finely granular and refined search explorations.
Beta users receive the following features: Login and password Save search constructs for later use in your own personal construct tables EigenOperator (cross Pollinating Operator) advanced features as described above (eigenvector to follow) Database (Table) upload and eigenvector computations EigenSearch seeks 300,000 beta testers for its advanced eigenOperator based cognitive engine. This engine will allow for a multiplicity of search parameters for users to select so as to mathematically narrow results. The system will employ eigenVectors, eigenValues and eigenMatrices to determine relevance to user searches; thereby rendering high fidelity confirmed search results. Naturally the computational power for doing such math is why beta testers are required. Each tester is welcome to comment on user friendliness, speed, change and ergonomic elegance. It is an eigenSearch goal to continue advancing the user interface so as to remain intuitively simple to use while at the same time providing hi-fidelity explorations. All beta testers will receive a login and password, which provides entry into features for saving search constructs and parameters according to their own classification approach. Saved results and parameters can be used at any time and modified to alter search results. Beta users will be able to import their own data sets (2-dimentional) and perform an eigenValue analysis.

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