I'm trying to do a system to match couples. A person must complete a form with age, country, hobbies, preference, sex with all of these data I'd like to know who's the best person to match with another person. I'll use clusters with WEKA so i need to know the best person to match with another person. I need some suggestions to do this. Someone knows some similar work to read, test or have any other ideas?
Thanks Greetings
This will not work. Matchmaking needs much more psychology.
Also, clustering is the wrong tool. I guess your idea is to use similarity search. Whst do you need groups for? What would you do if your analysis reveals there id only one group in your data, or if there are clusters with 1 element only?
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Beginner question. I would like to have a value list display only the records in a found set.
For example, in a law firm database that has two tables, Clients and Cases, I can easily create value list that displays all cases for clients.
But that is a lot of cases to pick from, and invites user mistakes. I would like the selection from the value list to be restricted to cases matched to a particular client.
I have tried this method https://support.claris.com/s/article/Creating-conditional-Value-Lists-1503692929150?language=en_US and it works up to a point, but it requires too much entry of data and too many tables.
It seem like there ought to be a simpler method using the find function. Any help or ideas greatly appreciated.
I have a unique problem and I'm not aware of any algorithm that can help me. Maybe someone on here does.
I have a dataset compiled from many different sources (teams). One field in particular is called "type". Here are some example values for type:
aple, apples, appls, ornge, fruits, orange, orange z, pear,
cauliflower, colifower, brocli, brocoli, leeks, veg, vegetables.
What I would like to be able to do is to group them together into e.g. fruits, vegetables, etc.
Put another way I have multiple spellings of various permutations of a parent level variable (fruits or vegetables in this example) and I need to be able to group them as best I can.
The only other potentially relevant feature of the data is the team that entered it, assuming some consistency in the way each team enters their data.
So, I have several million records of multiple spellings and short spellings (e.g. apple, appls) and I want to group them together in some way. In this example by fruits and vegetables.
Clustering would be challenging since each entry is most often 1 or two words, making it tricky to calculate a distance between terms.
Short of creating a massive lookup table created by a human (not likely with millions of rows), is there any approach I can take with this problem?
You will need to first solve the spelling problem, unless you have Google scale data that could allow you to learn fixing spelling with Google scale statistics.
Then you will still have the problem that "Apple" could be a fruit or a computer. Apple and "Granny Smith" will be completely different. You best guess at this second stage is something like word2vec trained on massive data. Then you get high dimensional word vectors, and can finally try to solve the clustering challenge, if you ever get that far with decent results. Good luck.
I use Facebook and Google maps to get a full Geo Entities data values (country, city, street, zip...).
I store these values on my mongoDB,
I noticed that some locations are deffer in the way they were written on Face and on Google, for (an unreal) example Face wrote the name of 'Hawaii' with an 'e' - Haweii.
I use match_all fields (country + city + street...) to search for entities at the same location but since some are written a bit different i will not find them.
Is there a way make elasticsearch search for 'Hawaii' and any other option that sounds like Hawaii but written a bit different?
Thanks for any help!
Using Google API one can get a location's
full details
To match words that sound similar you can use the phonetic analyzer. You can also give fuzzy query a try to match words with spelling mistakes. None of them are fool proof though and may result in false positives. Guess you'll have to experiment a little to come up with a solution that best fits your need.
If you have a known set of differences between Facebook and Google maps, you could look at using Synonyms at either index time or query time to accommodate differences in the APIs; There are merits to taking either approach.
I'm trying to understand the idea of noSQL databases, to be more precise, the concept behind neo4j graph database. I have experience with SQL databases (MySQL, MS SQL), but the limitations of managing hierarchical data made me to expand my knowledge. But now I have some questions and I can't find their answers (maybe I don't know what to search).
Imagine we have list of countries in the world. Each country has it's GDP every year. Each country has it's GDP calculated by different sources - World Bank, their government, CIA etc. What's the best way to organise data in this case?
The simplest thing which came in mind is to have the node (the values are imaginary):
China:
GDPByWorldBank2012: 999,
GDPByCIA2011: 994,
GDPByGovernment2012: 1102,
In relational database, I would split the data in three tables: Countries, Sources and Values, where in Values I would have value of GDP, year, id of the country and id of the source.
Other thing which came in mind is to create nodes CIA, World bank, but node Government looks really weird. Even though, the idea is to have relationships (valueIfGDP):
CIA -> valueOfGDP - {year: 2011, value: 994} -> China
World Bank -> valueOfGDP - {year: 2012, value: 999} -> China
This looks pretty weird for me, what is more, what happens when we add the values for all the years from one source? We would have multiple relationships or what?
I'm sorry if my questions are too dumb and I would be happy if someone explain me or show me what book/article to read.
Thanks in advance. :)
Your questions are very legit and you're not the only one having difficulties to grasp graph modelling at first ;)
It is always easier to start thinking about the questions you wanna answer with your data before modelling it up front.
Let's imagine you wanna retrieve the GDP of year 2012 computed by CIA of all countries.
A simple way to achieve this is to label country nodes uniformly, and set an attribute name that obviously depends on the country name.
Moreover, CIA/WorldBank/Government in this domain are all "sources", let's label them uniformly as well.
For instance, that could give something like:
(ORGANIZATION {name: CIA})-[:HAS_COMPUTED_GDP {year:2011, value:994}]->(COUNTRY {name:China})
With Cypher Query Language, following this model, you would execute the following query:
START cia = node:nodes(name = "CIA")
MATCH cia-[gdp:HAS_COMPUTED_GDP]->(country)
WHERE gdp.year = 2012
RETURN cia, country, gdp
In this query, I used an index lookup as a starting point (rather than IDs which are a internal technical notion that shouldn't be used) to retrieve CIA by name and match the relevant subgraph to finally return CIA, the GDP relationships and their linked countries matching the input constraints.
Although Neo4J is totally schemaless, this does not mean you should necessarily have a totally flexible data model. Having a little structure will always help to make your queries or traversals easier to read.
If you're not familiar with Cypher Query Language (which is not the only way to read or write data into the graph), have a look at the excellent documentation of Neo4J (Cypher: http://docs.neo4j.org/chunked/stable/cypher-query-lang.html, complete: http://docs.neo4j.org/chunked/stable/index.html) and try some queries there: http://console.neo4j.org/!
And to answer your second question, if you wanna add another year of GDP computations, this will just boil down to adding new relationship "HAS_COMPUTED_GDP" between the organizations and the countries, no more no less.
Hope it helps :)
I am trying to match objects based on predefined user preferences. A simple example would be finding best matching vechicle.
Lets say a user 'Tom' is offered a rented vehicle for travel based on his predefined preferences. In this case, the predefined user preferences will be -
** Pre-defined user preferences for Tom:
PreferredVehicle (Make='ANY', Type='3-wheeler/4-wheeler',
Category='Sedan/Hatchback', AC/Non-AC='AC')
** while the 10 available vehicles are -
Vechile1(Make='Toyota', Type='4-wheeler', Category='Hatchback', AC/Non-AC='AC')
Vechile2(Make='Tata', Type='3-wheeler', Category='Transport', AC/Non-AC='Non-AC')
Vechile3(Make='Honda', Type='4-wheeler', Category='Sedan', AC/Non-AC='AC')
;
;
and so on upto 'Vehicle10'
All I want to do is - choose a vehicle for Tom that best matches his preferences and also probably give him choices in order, i.e. best match first.
Questions I have :
Can this be done with Mahout Taste?
If yes, can someone please point me to some example code where I can start quickly?
A recommender may not be the best tool for the job here, for a few reasons. First, I don't expect that the best answers are all that personal in this domain. If I wanted a Ford Focus, the best alternative you have is likely about the same for most every user. Second, there is not much of a discovery problem here. I'm searching for a vehicle that meets certain needs; I don't particularly want or need to find new and unknown vehicles, like I would for music. Finally you don't have much data per user; I assume most users have never rented before, and very few have even 3+ rentals.
Can you throw this data at a recommender anyway? Sure, try Mahout Taste (I'm the author). If you have the book Mahout in Action it will walk you through it. Since it's non-rating data, I can also recommend the successor project, Myrrix (http://myrrix.com) as it will be easier to set up and run. You can at least evaluate the results to see if it's anywhere near useful.
Either way, your work will just be to make a CSV file of "userID,vehicleID" pairs from your data and feed it in. Then it will give you vehicle IDs as recommendations for any user ID.
But, I imagine you will do much better to analyze what people picked when the car wasn't available, and look at the difference, and learn which attributes they are most and least likely to be sacrificed, and learn to score the alternatives that way. This is entirely feasible since this data set is small, and because you have rich item attribute data.