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Difference between internet intranet and extranet

Difference between internet intranet and extranet
Compare internet, intranet and extranet.

Differentiate/Compare/ Difference between internet intranet and extranet

Internet-Intranet-Extranet

Internet, Intranet & Extranet

Internet

Intranet

Extranet

It is a Global system of interconnected computer network.

It is a Private network specific to an organisation.

It is a Private network that uses public network to share information with suppliers and vendors.

Not regulated by any authority.

It is regulated by an organization.

It is regulated by multiple organization.

Thus content in the network is accessible to everyone connected.

Thus content in the network is accessible only to members of organization.

The content in the network is accessible to members of organization & external members with access to network.

It is largest in terms of number of connected devices.

It is small network with minimal number of connected devices.

The number of devices connected is comparable with Intranet.

It is owned by no one.

It is owned by single organization.

It is owned by single/multiple organization.

It is means of sharing information throughout the world.

It is means of sharing sensitive information throughout organization.

It is means of sharing information between members and external members.

Security is dependent of the user of device connected to network.

Security is enforced via a firewall.

Security is enforced via a firewall that separates internet & extranet.

Example: What we are normally using is internet.

Example: TCS using internal network for its business operations.

Example: HP and Intel using network for business related operations.

Users can access Internet anonymously.

Users should have valid username/password to access Intranet.

Users should have valid username/password to access Extranet.

Internet is unregulated and uncensored.

But Intranet is regulated by the organization policies.

Extranet is also regulated by contractual agreements between organizations.

Contributed by: Abishek Ganesh


Note: The above differences have been derived through a proper understanding. So please share the link of this webpage as “sharing is a way of spreading knowledge”. But, please do not copy & paste it in other Website or Forums.

Hey guys we are eager to hear from you, about your views and your thoughts. Let us know how much you like the differences mentioned above. Please give your comments and if possible share because “Sharing is Caring”.

TCS Interview Experiences

TCS Interview Experiences/ TCS Interview Experience

Experience-1

Contributed By: Aditya Rao

The interviewers i had were quite friendly. More importantly, they knew what they were doing and could very well gauge how the person in front of them was. They looked at marksheets and asked questions based on the subjects in which marks were comparatively higher. They also had very clear questions on projects undertaken. All the questions tested not only concepts but also attitude towards learning. I would suggest being as frank as you can in front of them. They can see through visages as they are very experienced in interviews. The next would be to have a clear understanding about the subjects in which you scored comparatively higher marks, as also about the why’s and how’s of your project.


Experience-2

Contributed By: Anuja Panicker

The experience was a very pleasant one.For tips: The most important is that you should be confident in the way you speak and present yourself. Know yourself and answer truthfully. These are people who can sniff a lie when it’s being said. Be true and honest, be prepared with technical questions ( basics) and show eagerness to learn. Know about the company and be updated.
They want young, enthusiastic and smart learners who can be moulded to fit their company.


Experience-3

Contributed By: Pooja V

TCS is indeed a great experience. The rounds are quite simple as compared to the hype created for it. There were 3 rounds : Online Aptitude test,Technical interview,HR interview.

For Aptitude : Refer TCS mock test on their site(The actual test was simple as compared to that),Refer previous years apti questions from various online sites as most of the questions were repeated.The questions mainly consist of quants and logic. Time management is important and as every question has negative marking it is advisable to attempt what you are sure of.

Next up was,
Technical and management interview :
One tip :Be confident and smile throughout! The moment you show stress,you lose.

Technical Questions:
Basic java programs, fav subject,how usb port works,data transportation,B.e project based, etc.

Management Questions :
Why TCS,Are you ready to work in any part of the world,hobbies,describe yourself,why are you in IT,What would you do with your first salary,etc.

HR Interview:
Describe your projects in simple language,hobbies,Will you work anywhere across the globe,few basic questions about TCS,How ambitious are you,life goals,best achievement,etc.

Final Word :
Stay Calm,believe in yourself and answer the best you can.


Experience-4

Contributed By: Anonymous

Excellent experience , it was my first career interview of life and to clear it certainly brings a huge smile on the face. My tips for juniors will be :-
For Technical
Be sure with the language you written in resume . Be 90% sure with that language. You can or will be asked to write programs. Be very sure about

  • basic differences of java vs c++
  • Interpretation vs compilation

and in HR :- if you are in some organizing committee then be prepared with questions like
what was your role, what problems you faced how you overcame etc. In the end just keep 2 things in mind body language and confidence .


Experience-5

Contributed By: Uma Mounika Karukonda

Questions based on basics of principles of communication were asked. I was asked to speak about myself bringing out my best qualities. Later I was questioned were I would comfortable working in any city away from Mumbai with not so flexible working hours.

I would suggest to brush through whatever basics that have been taught and confidence is the key.


Experience-6

Contributed By: Madhura Milind Nagaonkar

Technical interview was fully based on programming and real time applications.
Few questions were based on BE project.
confidence is important to crack HR round.


Experience-7

Contributed By: Maximus

1. Be confident .
2. Being confident shudnt be confused with being arrogant .
3. Try to be as humble as possible and don’t mess up.
4. If u don’t know then don’t try and lie ur way out of it if u can’t .
5. If u do well and good u will get selected :p


Thankyou guyz for supporting and providing the invaluable source of Information and experience. These are the placement and interview experiences of TCS campus recruitment batch-2015. All of these experiences are genuine and this has been only possible through the support of the contributors.

Guy if anyone wishes to provide their TCS interview experiences can do so by clicking the following link
Click here.This would be of great help.

Notice:
Anyone wanting to use or copy these experiences in their websites or forums need to take prior permission from the conceptsimplified.Failing to adhere would lead to legal contempt.

Association Rule Mining

Association Rule Mining

Apriori Algorithm

Hi guys welcome back, in this post I would be covering about Apriori algorithm’ s association rules, its disadvantages and more specifically its limitations. First of all I would deal with pseudo code of Apriori algorithm.

Pseudo Code: (Ignore this if you get confused the steps in previous post are sufficient)
Ck: Candidate itemset of size k.
Lk: Frequent itemset of size k.
L1= {contains frequent items}
For (k=1; Lk! = null; k++)

    1. Ck+1 = candidates generated from Lk (through join operation);
    2. For each transaction t(say T100, T200, ..) in database increment the count of all candidates in Ck+1 that are contained in t;
    3. Lk+1= candidates in Ck+1 with min_support;

End
Return Union (Lk);

What is Association rule?
Representation: computer ➞ Antivirus software
Association depicts whether purchase of computer is associated with antivirus software. That is, Is Antivirus software bought frequently provided customer buys computer? This is what association is.

Association rule thus describes whether a particular item on Right hand side of the arrow is bought frequently with item on left hand side. Association uses the concept of confidence.Confidence is the minimum accepted percentage that depicts whether particular item is frequently purchased with other item. So if an association has confidence >= min_confidence specified then it is said to be strong association. (Again don’t worry this would be clearer in the below example)

Confidence = support_count(l)/support_count(s)

So from the previous post “Click here” we got Lk = {(I1, I2, I3), (I1, I2, I5)}

Generating Association Rules:

For each frequent itemset L (L refers to individual elements of Lk i.e. (I1, I2, I5) or (I1, I2, I5)) generate all non empty subsets of L.
s’ refers to item on left hand side and is usually subset of L.

General Association rule is depicted by S ➞ (L-S)

Considering L= (I1, I2, I5)
Subsets possible = ({I1}, {I2}, {I5}, {I1, I2}, {I2, I5}, {I1, I5})

When s= {I1} association rule is depicted by {I1} ➞ {I2^I5} that is from S ➞ (L-S)
Confidence=support_count(L)/support_count(s) = 2/6 =33.33% < 50% Support_count(I1,I2,I5)=2 Support_count(I1)=6 from previous post-Apriori Algorithm example.
So the above association {I1} ➞ {I2^I5} is not strong association.

Similarly calculate confidence for {I2} i.e. s= {I2}
{I2} ➞ {I1^I5} confidence=support_count(I1, I2, I5)/support_count(I2)= 2/7 =28.5% < 50% So again this is not a strong association {I5} ➞ {I1 ^ I2} confidence=2/2 =100% so greater than 50%
So above association is a strong association.

{I1 ^ I2} ➞ {I5} confidence=support_count(I1, I2, I5)/support_count(I1,I2)=2/4 =50%
{I1 ^ I5} ➞ {I2} confidence=2/2 =100% >50% hence strong association.
{I2 ^ I5} ➞ {I1} confidence=2/2=100% >50% hence strong association.

Now follow the same procedure to calculate association rule mining for L= {I1, I2, I3} in the similar way.

Limitations of Apriori Algorithm
1. It involves generation of candidate set of size k. It is not feasible to generate huge candidate set of k for large number of items and that too for large number of transactions/ records.
2. Further the database needs to be repeatedly scanned and checked for a large set of candidates by pattern matching which is again a performance issue in case of large number of transactions.

This has led to creation and usage of new algorithm for frequent item mining known as Frequent pattern growth (FP growth method)


Note:That’s it, for any doubts and queries you can post in the comment section below. Keep visiting and sharing this page because “Sharing is caring”.

Apriori Algorithm

Apriori Algorithm

  • It is a scalable mining method that is it can be applied on large number of transactions.
  • It is mining algorithm designed to extract frequent itemsets.
  • The items that occur frequently in the transactions are knows as frequent items.
  • Apriori algorithm uses the prior knowledge of frequent itemset properties, hence apriori.

Certain terminologies

  • Ck: Candidate itemset of size k
  • Lk: Frequent itemset of size k
  • Transaction-Id: basically is reference to a transaction, like say receipt number of customer that would be stored in a database on buying certain items from shopping mart or mall.
  • Items: These are the commodities that are purchased like say bread, milk. In below example I1, I2… represent items.
  • Support:It states the minimum number of count of particular item or itemset that is permitted.

I would be soon explaining what Ck and Lk are once I give out the steps of the algorithm.For proper understanding we would directly dive into an example.In this case we are considering support to be 2(Support=2)

Table-1

Transaction-Id Items
T100 I1,I2,I5
T200 I2,I4
T300 I2,I3
T400 I1,I2,I4
T500 I1,I3
T600 I2,I3
T700 I1,I3
T800 I1,I2,I3,I5
T900 I1,I2,I3

(Don’t worry if you are not able to decipher these steps )

General Apriori Algorithm Steps

  • Step1: Find frequent itemset Lk+1.
  • Step2: This step is called join step. In this Ck is generated by performing join operation. A kind of cross multiply operation you may have seen in SQl. That is (Lk+1* Lk+1). No worries if you have not understood this would be clear once you start seeing the example.
  • Step3: Pruning step, in this we remove certain items based on their count.
  • Step4: A frequent itemset Lk has been achieved.

Now, moving back to our example, in this T100 represents transaction number.Initially the algorithm will scan the database and prepare a candidate set-1 (C1) table where k=1. This table basically contains the item count for each item. From Table-1 it is clear that there are five items namely I1, I2, I3, I4 and I5.

C1

Item Count
I1 6
I2 7
I3 6
I4 2
I5 2

In C1 basically we have count of the items that are present in all of the transactions. From this C1, L1 is computed or generated.

L1

Item Count
I1 6
I2 7
I3 6
I4 2
I5 2

In case if the C1 (candidate itemset1) had any item less than support count(less than 2) that would have been removed or pruned. Say if I5 were 1 it would be not there in the L1 table.

Now moving ahead C2 is generated from L1. C2 is obtained by performing join operation on L1. So the candidate set formed is candidate set of order which contains two items. In this a pair can appear only once for instance I1, I2 is considered same as I2, I1.

C2

Item Count
I1,I2 4
I1,I3 4
I1,I4 1
I1,I5 2
I2,I3 4
I2,I4 2
I2,I5 2
I3,I4 0
I3,I5 1
I4,I5 0

Now pruning is performed to obtain L2 from C2. Thus L2 contains frequent items with count >= 2.

L2

Item Count
I1,I2 4
I1,I3 4
I1,I5 2
I2,I3 4
I2,I4 2
I2,I5 2

The count is obtained from Table-1 that is how many times (I1, I2) have occurred together. So it is clear that {I4, I5}, {I1, I4}, {I3, I4}, {I3, I5} have count less than 2 so they are not placed in L2 table above.Now generate C3 that is candidate set of size 3. That is each itemset can now contain 3 items.

C3

Itemset Count
I1,I2,I3 2
I1,I2,I5 2
I1,I2,I4 1
I1,I3,I5 0
I2,I3,I4 1
I2,I4,I5 0
I1,I3,I4 0

C3 is built by join operation on L2 that is (L2*L2). So in this itemset of 3 is formed by joining (I1, I2) and (I1, I3) so itemset created would contain (I1, I2,I3). In other words in case if there is one common item we remove and if say we had (I1, I2) and (I3, I4) then it would not be put into C3 that is it would have been ignored. Similarly (I1, I2) and (I1, I5) are joined to form (I1, I2, I5) and its count is noted from Table-1.(I1, I2) and (I1, I3) are joined to form (I1, I2, I3) so its count is 2. In the similar fashion whole C3 is built.

Now L3 is built by pruning:
L3

Itemset Count
I1,I2,I3 2
I1,I2,I5 2

Proceeding further C4 is built from L3 through join operation. That is L3*L3. Since it is C4 it should contain four items in itemset.

C4

Itemset Count
I1,I2,I3,I5 1

Now L4 will have no records or itemset because count of (I1, I2, I3, I5) is 1. L4 is said to be null.The frequent itemset by definition is Lk-1 when Lk becomes null.So L4 is null the frequent itemset by definition is L3

So in other words itemset {I1, I2, I3} and (I1, I2, I5) are the most frequently brought items.This was the glimpse of Apriori algorithm. In the next post I would be giving insights on Pseudo code of Apriori algorithm and Generation of Association rules.”Click here”


Note: Hey guys please do share this knowledge because “Knowledge Grows on Sharing” but do not copy & paste to other forums or websites. If you like this do give a facebook share and fb like. Stay tuned !!!

HITS Algorithm

HITS Algorithm

  • HITS Algorithm stands for Hyperlink Induced Topic Search Algorithm.
  • HITS Algorithm classifies relevant web pages as authorities and hubs given a certain search query.
  • It is query independent.
  • It has been implemented by ASK.COM search engine.

Reason and concept behind the development of HITS Algorithm was:

  • Prior to HITS Algorithm there was a text based ranking system.
  • So, for a given query (search), keyword matches were done.
  • And the document with most occurrences of keyword appeared in the result.
  • This was ridiculous and often returned irrelevant data.
  • Also it lacked synonymic capacity.
  • For instance, User may refer “automobile” for a car or vehicle.
  • Finally, it would also return WebPages with just word “automobile” billion times as the first result. So we can imagine how ridiculous it was?

So in order to tackle these anomalies HITS Algorithm was introduced. It used the link structure of web in order to discover and rank relevant pages. To understand this lets take an example:

  • Consider an example that user wants to search for top automobile manufacturers in last 2 years.
  • The user may be expecting the list of top car brands as the result of search.
  • However from the perspective of user an automobile is car, but for computer automobile is just an automobile.
  • There needs to be mapping required semantically (automobile = car).
  • However again, it is useless since searching remains stills text based and further car manufacturer may not use automobile in their description.
  • So there needs to be different mechanism for ranking.
  • So the concept of Hub and Authority was introduced which forms the basis of HITS.

Concepts

  • Page ‘i’ is called an authority for the query if it contains valuable information on subject.
  • Official websites of carmakers can be considered AUTHORATIVE.
  • These are the ones that are truly relevant to the query.
  • There is SECOND category of pages relevant in process of finding the AUTHORATIVE pages, these are called HUBS.
  • HUB’s role is to advertise the AUTHORATIVE pages.
  • They point the search engine in right direction.
  • HUBS even can be blogs describing the automobiles.

HITS Algorithm identifies good Authorities and Hubs by assigning two weights to pages namely:

    a.Authority weight/ranking:

    • These are pages with many in-links (many links pointing to particular URL).
    • Also they are the pages pointed by pages with HIGH HUB WEIGHT.

    b.Hub weight/ranking:

    • These are the pages with many out-links. (Pointing or referrers to other sites).
    • These pages serve as organiser of information/topic.
    • Also pages that point to pages with HIGH AUTHORITY have HIGH HUB WEIGHT.

AUTHORATIVE and HUBS have mutual reinforcement relationship.

That’s it for this post more information regarding working of the HITS Algorithm and its Advantages & Disadvantages would be covered in my next post. “Click Here”.


Note: Hey guys please do share this knowledge because “Knowledge Grows on Sharing” but do not copy & paste to other forums or websites. If you like this do give a facebook share and fb like. Stay tuned !!!

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