the question mentioned is as it is....i just copy pasted it here.
@saurabh thanx for the explainaton of the cube problem i guess that is an
appropriate soln for the question.
and for the other question on detection of typos and  suggestion i would
like to know to know what 'k' in your explaination stands for?how are the
values allocated to it ? should it be for each wrong word not mentioned in
the dictionary we got to check if the word exists with edit distance equal
to 1 in dictioanry
and so on until we get the correct word???



on Sat, Oct 27, 2012 at 8:12 AM, Saurabh Kumar <srbh.ku...@gmail.com> wrote:

> could you please share the link? coz at first glance a Trie looks like a
> bad choice for this task.
>
> I'd go with the Levenshtein distance and a kd-tree.
> First implement the Levenshtein distance algorithm to calculate the edit
> distance of two strings.
> Second, since Levenshtein distance qualifies as a metric space we can use
> a metric tree like BK-tree to populate it with our dictionary.
> Choose a random word from dictionary as a root and subsequently insert
> dictionary words(picking them up randomly) into the tree.
> A node has arbitrary no. of children. The parent-child edge represents the
> corresponding Levenshtein distance between them.
>
> Building the tree is one time process. Once the tree is built we can
> devise a way to serialize it and store it.
>
> Using this tree we can find all the words with edit-distance less than or
> equal to, say k.
> Lets, define a function call in Tree class as: List KDTreeSearch(s, k);
> which searches for all strings s' in the tree such that |s-s'| <= k i.e.
> all strings which are less than or equal to an edit distance of k.
> Searching:
> Start with the Root and calculate the edit-distance of s from root. If
> its', say d then we know exactly which children we need to descend to in
> order to find the words with distance <=k.
>
> Looking for typos:
> Scan the document and for each word 'w' make a call: list =
> KDTreeSearch(w, 0);
> if, list.size() = 1. //We have the word in dictionary.
> else, list = KDTreeSearch(w, 2); // searching for all words with edit
> distance of 2 from w
>
> returned 'list' can sometimes be large, we can subsequently filter it out
> by narrowing down our definition of 'typos'
> e.g. for typo w = REDT [REST is more likely than RENT] or maybe some
> Phoneme model etc.... you should discuss this at length with the
> interviewer.
>
> On 27 October 2012 07:03, Raghavan <its...@gmail.com> wrote:
>
>> By any chance did you read the new blog post by Gayle Laakmaan..
>>
>> I guess to detect typos we can use some sort of Trie implementation..
>>
>>
>> On Fri, Oct 26, 2012 at 7:50 PM, payal gupta <gpt.pa...@gmail.com> wrote:
>>
>>>
>>>    Given a cube with sides length n, write code to print all possible
>>> paths from the center to the surface.
>>>    Thanx in advance.
>>>
>>>
>>>    Regards,
>>>   PAYAL GUPTA,
>>>   NIT-B.
>>>
>>>
>>>
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>>
>>
>> --
>> Thanks and Regards,
>> Raghavan KL
>>
>>
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