Consensus clustering
Hey there! Let me tell you all about consensus clustering in a very easy-to-understand way.
Now imagine you have a lot of toys and you want to group them together based on similar colors. But there are so many colors and different toys that it can be confusing to just group them all together.
That's where consensus clustering comes in!
Think of it like asking your friends to help you group your toys. You ask one friend to group them based on color, and another friend to group them based on shape, and maybe another friend to group them based on size.
But how do you know which groupings are the best? That's where the consensus part comes in. Consensus clustering is when you take all of the groupings from your different friends and find the ones that are the most similar.
So, you look at which toys were grouped together the most often by your friends, and you say those are the final groupings. This helps you get a more accurate grouping of your toys based on different criteria.
In real life, scientists use consensus clustering to group data in different ways, just like we grouped toys by color or size. They might use different criteria to group the data, like similarity in genes or similarity in protein structures. And just like we found the most similar groupings of toys, scientists find the most similar groupings of data using consensus clustering.
Hope that helped you understand consensus clustering!
Now imagine you have a lot of toys and you want to group them together based on similar colors. But there are so many colors and different toys that it can be confusing to just group them all together.
That's where consensus clustering comes in!
Think of it like asking your friends to help you group your toys. You ask one friend to group them based on color, and another friend to group them based on shape, and maybe another friend to group them based on size.
But how do you know which groupings are the best? That's where the consensus part comes in. Consensus clustering is when you take all of the groupings from your different friends and find the ones that are the most similar.
So, you look at which toys were grouped together the most often by your friends, and you say those are the final groupings. This helps you get a more accurate grouping of your toys based on different criteria.
In real life, scientists use consensus clustering to group data in different ways, just like we grouped toys by color or size. They might use different criteria to group the data, like similarity in genes or similarity in protein structures. And just like we found the most similar groupings of toys, scientists find the most similar groupings of data using consensus clustering.
Hope that helped you understand consensus clustering!