GRE Frequent Word-3

warrant /n/- the legal document form the court that authority can arrest someone.
unwarranted/adj/- not justified (without any reason)
esteem /n/:- Great respect
justified/adj/- with reasons
Feigned /adj/- fake eg. feigned interest
disguised/adj/- real eg disguised interest (unfeigned)
undue/adj/- sufficiently large(excessive) eg. undue praise/critisim
contempt/n/
           - dislike eg. unfeigned contempt.
           -contempt of court
Introverted/adj/
           -not willing to speak more eg. introverted natures.
fondness/n/
            -strong liking eg. Bush's fondness for travel is well known.
ceaseless/adj/
             -not stoppable(continuous) eg. ceaseless explorations
disdain/n/
            - contempt eg disdain for other art.
disdainful/adj/
            -disliking (thinking that they don't deserve your interest).
deceive /v/
      -keep the truth hidden form other for your own advantages eg deceive yourself.
wanderings/n/
       - Time spent travelling around eg. wandering around America
Self-proclaimed/adj/
        -self declare eg Devkota was a self-proclaimed poet of the people.

preconceptions/n/
       -concept before experience eg. The preconception about earthquake was wrong.
At large/phr./
       - as a whole/of many different subject/out of capture eg. Criminal was at large.
Rambling and unconstrained /phr./
      - Very broad
Rambling/adj/
      -too long and confusing
Forays/n/
      - A journey in order to explore
      -Raid/First attampt
Realm/n/
      -an area of interest
Aphoristic/adj/
    -- containing truth
Tantamount/n/
     -having the same effect as st. eg her refusal to answer is tantamount to guilt.
Exert/v/
     -to use st. such as power or authority in order to happen st.
Reclusive/adj/
     - preferring isolation ( eg. reclusive person)

Good Luck to Your GRE preparation... See you next time..

Data Mining and Data warehousing: Issues and Challenge

What is data?

A representation of facts, concepts, or instructions in a formal manner suitable for communication, interpretation, or processing by human beings or by computers

What is Data Mining?

Word Meaning

·         the practice of examining large databases in order to generate new information.

Definition:

·         Art/Science of extracting non-trivial, implicit, previously unknown, valuable, and potentially useful information from a large database

 

An Introduction to Data mining and data warehousing can be found here

Distributed System: A shared Approach

We are living in the age of information. Information has been expanded exponentially. This is causing some really challenging hardship to IT industry as well as academia. We need new techniques and tools to manage large  volume of data, Big data. Form this prospective, the concept of distributed system were emerged. The primary objective of this concept was to reduce the computation time on large amount of data and sharing of resources. Later on the concept of grid computing , more specialized form of distributed computing became buzzword in IT field. At present these concept were merged into new concept such as big data, machine learning and cloud computing. NO body want bye a mainframe or rack server, instead they bye  or leased required resource on cloud infrastructure managed by AMAZON or Microsoft AZURE. It cost minimum as well as required no time for set up and configurations.

On this regards, I have prepared a small presentation on Introduction to distributed system, lecture given at CDCSIT, TU, First Semester. The power point slide can be reached  here. The reference taken  in preparing these slide was the text book " operating system concept" by  Abraham Silberschatz.

Recommendation system: A collaborative approach

A recommendation system analyze your preference and automatically suggest you the similar item/product that you may be interested in.
For example Movie recommendation system such as NETFLIX , Product recommendation system such as AMAZON and so. on.

In this tutorial, I will be talking about food items recommendation system using collaborative approach with example.


Collaborative approach  uses the idea of collaboration between users preference and finding the similarity between users preference. These measurement are used as a recommendation criteria.

This can be illustrate with following figure:

 

Lets consider 5 items and 4 users with following ratings:

Items	Ram	Shyam	Hari	Gopal
MoMO	5	0	0	0
PIZZA	5	0	?	0
BIRIYANI	?	2	5	?
NODDLES	0	4	0	4
CHICKEN ROLL	0	5	0	?

By intuition, we can find the dissimilarity between User "Ram" and "Shyam" then the other users. So we need an algorithm to find the similarity between users so that this similarity criteria can be used for the recommendation propose. Lets say ram(5,5, ?,0,0) is a  rating vector for user ram and shyam(0,0,2,4,5) is for shyam ans so.on.

Similarity measure by Jecard Distance: The Jeccard distance between vector A and Vector B is defined as:
sim(A,B)=  |A intersection B|/ |A union B|
For Example: the jeccard distance between Ram and Shyam is =4/9 i.e common items divided by total item rated.
 Here the problem with this approach is that it ignore the value of rating and just consider whether the rating is present or not.

The next option is cosine similarity. it is defined as
cos(A,B) =A.B/|A|.|B|
Now the cosine similarity between ram and sham is :
Ram =(5,5, ?,0,0)=(5,5, 0,0,0) here feel the unknown rating by zero.
Syam=(0,0,2,4,5)
Now sim(Ram, Shyam)=(5x0+5x0+0x2+0x4+0x5)+/sqrt(5x5+5x5) xsqrt(2x2+4x4+5x5) =0
i.e they are opposite to each other. it gives better estimation of similarity then jeccard in case of rating values. There are many improvements on cosine similarity such as centered cosine or Pearson correlations and so.on.

Rating Predication
suppose we want to predict the rating of user x to item i, then we select the N-most similar user to x who also have rated item i and then can take a average of rating of this item i by these N user as a rating for item i by user x. This is very simple approach. The other approach is to rake weighted average.

The approach explained above is user based collaborative filtering. Now the another version of collaborative filtering is item based approach, Here this is very similar to user based. We need to find the similarity between item to item and then predict the rating of item i to user x.

Frequent GRE words (Version 1)

Basic Words

1.      Escalates/v/: Become great worse or more serious. eg. His financial problems escalated after he become unemployed.

2.      Revise /v/: To look again to improve it. eg:  I was asked to revise my proposal before submitting it again.

3.      Plummet/v/: to decline sharply eg: plummet supply

4.      Augment/v/:-

a.       to increase eg the price augmented.

b.      to uplift eg the seat was augmented.

c.       to improve or make better eg the meal was augmented.

d.      to enlarge eg: The photograph is augmented.

5.      Soar/v/:- rocket or sky-rocket eg soaring price (figurative)

6.      jeopardize /v/: to endanger eg. jeopardize culture/ By failing her finals, teacher jeopardize his whole future.

7.      Composure: mental calmness. eg a composed pilot.

a.       /opp/ discomposure

8.      Abstract /n/

a.       summary eg the abstract of story

b.      Not representing a particular figure eg abstract art

c.       Not concrete eg abstract idea.

9.      Archaic /n/  :

a.       very old or outdated eg the archaic meaning of word.

10.  Perturb /v/: to worry someone

11.  ephemeral/adj/: transient/fugitive/ momentary/fleeting/evanescent

12.  Momentous/adj/ long lasting

13.  momentum /n/- encouragement/stimulus /incentive /drive/impetus

14.  Momentary /adj/- transient/ ephemeral

15.  Drivel /v/- to talk nonsense or nonsense

16.  Gainsaid /V/- to contradict or to deny.

17.  immutable /adj/- unchangeable system

18.  Specious /adj/- seemingly true but false

19.  spacious /adj/- having comfortable space

20.  Erratic /adj/-Fluctuating eg erratic oil price.

21.  Discrete /adj/.- Secret and sensible so that other may not realize and notice eg journalist was following the prime minister in a discrete manner.

22.  Surreptitious /adj/- secret so that other may not notice. eg surreptitious glance.

/syn/- furtive eg they exchange furtive smile.

23.  implicit /adj/- hidden

24.  Explicit /adj/- clearly stated.

25.  Explicate /v/ : explain with logic. explicable problem.

26.  boast /v/:-

a.       Brag eg don’t brag

b.      have (matter of pride) eg Nepal boast Mt. Everest.

c.       To express st. with energy and pride eg he boasted he won the match.

27.  lackadaisical /adj/- with no energy or enthusiasm.

28.  Garrulous /adj/ -Having a habbit of talking a lot in unimportant matter.

29.  talkative:- willing to talk a lot.

30.  skeptic /adj/- non believer/doubt the truth.