K means algorithm explained with example(Very Easy)

[@radhaingle4664]

Really thank you very much for uploading k-means algorithm...you made it really very easy to understand.... Thank you very much...

[@dipaktale6072]

Thank u very much I search your lecture this topic on you tube but that time you didn't upload . but today i get your important lecture thank you sir your every lecture important for us .

[@recordingsp7833]

Really you have made it look very easy...thanks for valuable knowledge of machine learning...pl upload the series of machine learning algorithms in the same way.... thanks a lot Yogesh..

[@prashantgore4725]

Thanks once again for uploading an important topic of machine learning... waiting for more

[@bharatsoft8886]

awesome.......

[@MR-cs6vo]

4 month lecturing in 8 months great i really understood the concept very easily explained smallest kid can understand this one thank you sir good teachers can not find in these days. you earned my subscribe

[@chandranks]

Super. I am learning R program. Your simple explanation make me understand the concept well Thanks

[@olatundeolukoya1788]

I got frustrated, I was like if I click on this and i don't understand, then I'm done with k-means, I was like is this guy going to explain better ? well let me try and you brought joy to my eyes. even after 4years, you still have a great impact on many of us. Thanks so much!!!!! you MADE LEARNING SO EASY...… CHEERS
BUT one question is, what is we have say 1000 random numbers, and we have like 20 k, how to do that ????

[@gabrielemarello5467]

Usefull to pass an exam at University; thank you

[@sandeepmane8694]

Very nice sir... easily explain to understand

[@rahulrajraj8231]

It's really easy to understand perfect. I am perfect to solve K means algorithm any no to eligible thanks for your uploading, Lot more videos to expect...Thankyou

[@usmanjaji7825]

Thank you very much for this video. It is perfectly simplified.

[@83vbond]

Easy to understand with nice and simple example. Thanks.

[@radouaneaarbaoui7206]

A very nice job. please do another video for the algorithm with multiple dimensions.

[@sivanikabhoomi8063]

Thank you so much.
It's really easy to understand.

[@zealit5404]

Thanks a lot for sharing...

[@ompatil2120]

absolutely simplified....thank you...

[@narasa12]

Excellent

[@rbyourfriend09]

great video

[@nileshmurumkar5067]

Thanks a lot once again...Just keep posting..

[@queenofmyworld2761]

Wowwwwwwwwww nyc sir tq

[@pragadeshv8268]

Really made easy to understood. Thanks a lot 👍

[@harshggupta13190]

Superb, super awesome

[@maheshaddipalli8593]

Good work sir

[@jacinthavijayakumar4955]

really explained in a simple manner , thankyou sir

[@01_mayuriadhao75]

Excellent 👌👌👌👌

[@mauanu100]

This tutorial is very simple and nicely explained.
In the same way could you explain the " Within cluster sum of squares by cluster , between_SS, total_SS "

[@yogeshmurumkar]

yes sure i will forward the link for wcss explanation and implementation ...... Also its use in Kmeans algorithm to decide 'k' value.....

[@sachinahankari]

Good video.. understand the concept within short span of time but you should also include python programming for this algo

[@yogeshmurumkar]

sure

[@pubuduchanna1736]

Thanks a lot for the explanation.

[@nileshsahastrabuddhe1946]

Excellent!!!!

[@ramsp35]

A very simple example and a nice elaboration of the concept. Can you also explain, what the result will be when a new element gets added after the clustering is complete. Say, if a new value 18 comes in, should we just find the nearest mean between m1 and m2 and add it to the appropriate cluster or reshuffle the set again? I know, I sound silly but want to know.

[@yogeshmurumkar]

Hi Ramanathan S.P. , I am glad if it helped you.To answer your question,if a new number 18 is added you should consider that from the start coz here the means or centroid are calculated considering all the data present initially..also k means always gives approximate results..

[@ramsp35]

@@yogeshmurumkar Thanks a lot...!!!

[@russellkemmit73]

You are an amazing teacher, have you thought about doing a video on "Maximum Likelihood" for finding the coefficients for logistic regression?

[@yogeshmurumkar]

I am glad if it helped you Russell....and yeah I will come with a video on Maximum Likelihood...

[@russellkemmit73]

​@@yogeshmurumkar That would be amazing!

[@Bobby-qc7qj]

underrated talent

[@agamkushwaha667]

Very Nice!!

[@Janani.G]

Please do video on differential privacy sir

[@kadhirn4792]

Thank you, it justifies the content

[@dinudavis8609]

Thanks man. good job !

[@kunchakurinandan9286]

Make a video on pam algorithm please!

[@padmaparshapu8884]

Thank you sir

[@nallababupenumutsa3679]

Thanks a lot sir ,thanq thanq............

[@aslambu023]

Thank you very much

[@akshaysahastrabuddhe1558]

excellent

[@surya.s4785]

Thank you boss

[@vanditaahire2852]

excellent...

[@jeevitham1806]

Thanks a lot ur video is really understandable and one defect it is sound is little bit low.

[@yogeshmurumkar]

Jeevitha M ...I am glad if it helped you...sound might be low as it's one of my first videos when I started with RUclips channel.. thanks for suggestion..I will definitely take care in future

[@user-oc2zy3xt4f]

Thank you so much 🌹🌹🌹🌹🌹🌹🌹🌹🌹🌹🌹🌹🌹🌹

[@vireshchapte9594]

very nice!!!!!!

[@bharatsiom]

wow

[@snehas2041]

thanks a lot

[@Chaitu0213]

K-means algorithm

[@alanboobalan1646]

Bro hierarchical clustering ku examples kutunga. bro neenga panathu Central clustering thana bro

[@sujithkumar2609]

sir take 10 and 20 for initial cluster we could get the answer quickly

[@yogeshmurumkar]

yes sujith thats possible ...here I just taken randomly for explanation....

[@sai6750]

What if we get same difference for some number while clustering??
Eg:while doing when m1=3 and me=18 to cluster 11 weget same difference.

[@yogeshmurumkar]

this is just an example to understand the concept...but in implementation we have methods to decide value of k and algorithms takes care that it will get different distances.....

[@dhevashree5344]

Sir I need explanation with some examples of data set then explain the k means algorithm in R Language that's more efficient manner I think so

[@yogeshmurumkar]

sure i will make it in R also...

[@SriKanth-mz3hr]

It's not mean you chose random , it should be a Centroid..hope you consider that

[@yogeshmurumkar]

sir this is an example to understand the concept...please see the implementation also...done in my other videos...

[@Shahad-tl6gr]

What if lik this example Apply K-Means approach to construct two clusters M1= (1,2,3) and M2= ( 0, -2, -5 ) of the following data ?
X1= (2,3,4) , X2=(1.5,2.7,3.1) , X3= ( 0.5, -3, -5 ) , X4= (2.6,3.5,4.8) , X5=(0.7,1.2,2.3) and X6= ( 0.1, -3, -4 ) ?

[@anirudhshankarkhedker4400]

sir I think your voice should be a little bit clear. otherwise great job sir

[@yogeshmurumkar]

Thanks for the suggestion...I will take care for the same ...as its a first of few videos when i started ... anyway thanks sai..

[@sherinjohn7496]

Here,where is the Euclidean distance???
Pls rply

[@Sonu-hu6ho]

when we are calculating k1 and k2 itself, euclidean distance / minimum distance is calculated. The elements in the problem set which are having minimum distance with mean is considered.

[@raghunandanr9013]

So M1 and m2 is centroid ?

[@yogeshmurumkar]

mean , here we have taken a simple example to understand k means.....yes we are assuming them as centroid here

[@raghunandanr9013]

@@yogeshmurumkar Can I have your linked in or any social media link

[@bharathKumar-or6gd]

Sir, ur making little bit confusing

[@karthikeyanr3543]

Konjam tamil aaa sollunga sir

[@raghunandanr9013]

Enkita kelu mapla na soldren 8220299484

[@ANGAYARKANNI_A]

thank you sir