Hierarchical Clustering intuition

Key Takeaways

hello on today we'll be discussing about a little nuclear strength intuition try to understand what is the maths behind I do plus true sorry hi little crust room in a previous video we have already seen something about k-means clustering algorithm but in this particular video we'll try to understand what some mats behind hide it'll last row and Heinkel clustering is one of the very good son supervised machine that works similar to a k-means clustering only the technique is completely addition so this is my high school West Wing all these things so this is my husband Russ tree now for this high school question how it works is that they said that this is my unsupervised machine learning technique this is the answer for a machine so initially I will be having some points so this point suppose I marketed them this black points right now you can see that I just have six points over here now what does what does it happen with respect to the six points you had chemistry first of all you try to find out each and every points are basically specified as different clusters energy so these all are different clusters now what will happen is that we've tried to find out the two nearest point or the two nearest cluster in this since we are considering each and every point as a single question so suppose these two points are with interest and this two points won't specify in the right hand side and another diagram which is called as dendogram so this is basically called as dendogram in the dendogram in the x axis I have points and the y axis I have distance now if I am considering this two point and I named it as p1 and p2 so pasa fighter of p1 and p2 and suppose the distance between this is point five what I am going to do is that I'm going to combine this and this will be fight so this is one type of dendogram that we have created for p1 and p2 then after that we try to find out the distance between this cluster and this point and try to find out which are the next me respond and from that I found out that it's to our than this point and here I get to specify this as e3 and peace right so what I'm going to do is that I'm going to define my another point that is e 3 and E 4 and suppose this distance is somewhere on one so I'm going to combine this and this distance will be now currently I've got two groups still there points and this again we have found out that these are the next nearest one so suppose I make it as e 5 P 6 then again I will be having something like C 5 and P 6 suppose I calculate the distance somewhere here it is coming around 1.5 I have combined this then finally I'll try to find out which will be the nearest clusters to this and suppose I found out that these two clusters are very very near what I'm going to do we're going to combine these two clusters where I have points like III p4 p5 pieces I can combine this together like this where my distance is actually do finally I'll combine this whole group as one cluster where I'll include B 1 and B 2 because these are my next nearest one suppose this 2.5 this p1 p2 will get connected here so this is how my dendogram looks like right but what is the main aims just remember guys this is an unsupervised machine learning technique and our main aim is to basically find out at what should be the exact number of clusters should I use in order to classify my point properly not classify instead group my parents properly the clustering works basically on the similarity of the treated similarity of the data is basically then we are calculating over here with respect to the distance and this distance is measured by something called as euclidean distance in my previous video also have disciplined about Euclidean distance this formula is basically given by if I have two points x1 y1 and x2 y2 I can give I can give it by square root of x2 minus x1 holster plus y2 minus y1 whole square so this will be the distance between two points p1 and p2 which represent your device 1 y1 and x2 right now the next thing is that how can I find out the number of exact clusters that I need to classify this problem so there is a simple hack that is used this hack is nothing that we need to find out how many groups we need to find out the longest vertical line such that none of the horizontal line passes through so I can't consider this particular line because there is a horizontal line that passes through this particular point similarly I can't consider this line also because there is a horizontal line passing through this point ok so similarly I can't consider this I can consider this I can't consider this also because there is a horizontal line I can consider this particular line because this may be the longest line compared to all the others what I do is that I'll draw a straight line that passes through this particular point and I'll try to see how many points it is passing so there are two points that is it is passing through so I can specify that the number of clusters that I can use for this problem is basically and this is the hack basically used for removal then this is just a rough diagram your clusters may also get changed to 3 that depends love how many points that is passing as soon as I find out the longest rotatory and once I am able to do this I will use an a scalar and try to use this at the clustering and I'll be able to group this whole data into two questions based on Similac based on euclidean distance so this was the whole idea about hiatal clustering I hope you like this particular video guys and please subscribe the channel if you have not already done and keep learning I'll see you up in the next video thank you one and all have a great day