- Clusters are collections of similar data
- Clustering is a type of unsupervised learning
- The Correlation Coefficient describes the strength of a relationship.
Clusters are collections of data based on similarity.
Data points clustered together in a graph can often be classified into clusters.
In the graph below we can distinguish 3 different clusters:
Clusters can hold a lot of valuable information, but clusters come in all sorts of shapes, so how can we recognize them?
The two main methods are:
- Using Visualization
- Using an Clustering Algorithm
Clustering is a type of Unsupervised Learning.
Clustering is trying to:
- Collect similar data in groups
- Collect dissimilar data in other groups
- Density Method
- Hierarchical Method
- Partitioning Method
- Grid-based Method
The Density Method considers points in a dense regions to have more similarities
and differences than points in a lower dense region.
The density method has a good accuracy. It also has the ability to merge clusters.
Two common algorithms are DBSCAN and OPTICS.
The Hierarchical Method forms the clusters in a tree-type structure.
New clusters are formed using previously formed clusters.
Two common algorithms are CURE and BIRCH.
The Grid-based Method formulates the data into a finite number of cells that form a grid-like structure.
Two common algorithms are CLIQUE and STING
The Partitioning Method partitions the objects into k clusters and each partition forms one cluster.
One common algorithm is CLARANS.
The Correlation Coefficient (r) describes the strength and direction of a linear relationship and x/y variables on a scatterplot.
The value of r is always between -1 and +1:
|-1.00||Perfect downhill||Negative linear relationship.|
|-0.70||Strong downhill||Negative linear relationship.|
|-0.50||Moderate downhill||Negative linear relationship.|
|-0.30||Weak downhill||Negative linear relationship.|
|0||No linear relationship.|
|+0.30||Weak uphill||Positive linear relationship.|
|+0.50||Moderate uphill||Positive linear relationship.|
|+0.70||Strong uphill||Positive linear relationship.|
|+1.00||Perfect uphill||Positive linear relationship.|
Perfect Uphill +1.00:
Perfect Downhill -1.00:
Strong Uphill +0.61: