74831 Mining the Web: eigenVectors, Kriging, Inverse Distance

Site and Features:  http://www.eigensearch.com

Search engine, eigenMethod, eigenvector, mathematical, manifolds, science, t
echnical, search tools, eigenmath, Jacobian, quantum, mechanics, manifolds, 
science, physics, chemistry, law, legal, government, home, office, business,
 domain lookup, medical, tr
avel, food, university students, searching, searchers, surfing, advanced sea
rch, search tools

Chemistry, mathematics, physical sciences, engineering, aerospace, astronomy
, photography, news, computers, software, investment, venture capital, stake
holder, Biology, Chemistry, Geosciences, Biotechnology, Medical, Nursing, An
thropology, psychology, psy
chiatry, Philosophy, History, Business, bachelor, Ph.D., Masters, administra
tive, MBA, eigenMethod, eigenvector, mathematical, manifolds, science, techn
ical, search tools, eigenmath, Jacobian, quantum, mechanics, manifolds, phys
ics, chemistry, law, legal,
health, government, home, office, business, domain, lookup, medical, travel,
 food, university, students, search, searches, search engine, directory, dir
ectories, category, categories, help,  searching, searchers, surfing, advanc
ed search, search help, se
arch tips


Beta Users and advanced features Sign-up here...  http://www.eigensearch.com/inc/c

Central to eigenSearch Advanced is the freedom to construct complex search e
xplorations, save the forms for later use; and apply weight factor to each p
hrase and term. EigenSearch processing will apply eigenvector math and Jacob
ian matrices to construct s
earch terms that are tailored to your exploration. Cross-pollination is also
 applied as described below. The eigenvector approach is clearly highly adva
nced and would normally be useful for very sophisticated applications. Never
theless, anyone may utilize
the method. An advanced form is simply a matrix in which the user types word
s 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 (unl
ike 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 contaminati
on of results
Illustration:
"Blood Hounds" + "English Breed" will present documents that contain either of these ph
rases within the same peer level in a document storage structure; for example, within t
he directory: www.smartdogs/hounds.
eigenSearch limits pollinating occurrences outside a peer level. For example; "blood ho
unds" + "English breed" found in two different directories would not report an eigenSea
rch result: i.e. "Blood hounds" found in www.smartdogs/hounds and "English B
reed"
found in. www.smartdogs/hounds/Europe would not be found. EigenSearch theref
ore searches one tree (peer) level in a site and looks for multiple occurren
ces of multiple phrases across all documents within this peer level.
Corporate products can be tailored to drill down infinite levels for eigenOp
erator (cross-pollinating operator) matching.
eigenSearch single phrase results will find all documents and show the resul
ts as independent findings. This way the user can find results across many d
ocuments and the combined highly constrained results are reserved for a sing
le level cross pollination.

Extremely high (cross-pollinating) eigenValues will correspond to finely gra
nular 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 ab
ove (eigenvector to follow)
Database (Table) upload and eigenvector computations
EigenSearch ss 300,000 beta testers for its advanced eigenOperator based 
cognitive engine. This engine will allow for a multiplicity of search parame
ters for users to select so as to mathematically narrow results. The system 
will employ eigenVectors, e
igenValues and eigenMatrices to determine relevance to user searches; thereb
y rendering high fidelity confirmed search results.
Naturally the computational power for doing such math is why beta testers ar
e required. Each tester is welcome to comment on user friendliness, speed, c
hange and ergonomic elegance. It is an eigenSearch goal to continue advancin
g the user interface so as
to remain intuitively simple to use while at the same time providing hi-fide
lity explorations.
All beta testers will receive a login and password, which provides entry int
o features for saving search constructs and parameters according to their ow
n classification approach. Saved results and parameters can be used at any t
ime and modified to alter s
earch results. Beta users will be able to import their own data sets (2-dime
ntional) and perform an eigenValue analysis.







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