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Consider the following example: A dataset is given by [1, 1, 4, 3, 5, 2, 6, 2, 4], and point P is
equal to 5. Figure 7-4 shows how kNN would be applied to this dataset.
Now, if you were to specify that k is equal to 3, there are, based on distance, three nearest
neighbors to the point 5. Those neighbors are 4, 4, and 6. So, based on the kNN algorithm, query
point P will be classified as 4 because 4 is the majority number in the k number of points nearest
to it. Similarly, kNN continues defining other query points using the same majority principle.
When using kNN, it’s crucial to choose a k value that minimizes noise — unexplainable
random variation, in other words. At the same time, you must choose a k value that includes
sufficient data points in the selection process. If the data points aren’t uniformly distributed,
it’s generally harder to predetermine a good k value. Be careful to select an optimum k value
for each dataset you’re analyzing.
Large k values tend to produce less noise and more boundary smoothing — clearer
definition and less overlap — between classes than small k values do.
Knowing when to use the k-nearest neighbor algorithm
kNN is particularly useful for multi-label learning — supervised learning where the algorithm is
applied so that it automatically learns from (detects patterns in) multiple sets of instances. Each of
these sets could potentially have several classes of their own. With multi-label learning, the
algorithm learns to predict multiple class labels for each new instance it encounters.
The problem with kNN is that it takes a lot longer than other classification methods to classify a
sample. Nearest neighbor classifier performance depends on calculating the distance function and
on the value of the neighborhood parameter k. You can try to speed things up by specifying optimal
values for k and n.
Exploring common applications of k-nearest neighbor algorithms
kNN is often used for internet database management purposes. In this capacity, kNN is useful for
website categorization, web page ranking, and other user dynamics across the web.
kNN classification techniques are also quite beneficial in customer relationship management
(CRM), a set of processes that ensure a business sustains improved relationships with its clients
while simultaneously experiencing increased business revenues. Most CRMs get tremendous
benefit from using kNN to data-mine customer information to find patterns that are useful in
boosting customer retention.
The method is so versatile that even if you’re a small-business owner or a marketing department
manager, you can easily use kNN to boost your own marketing return on investment. Simply use
kNN to analyze your customer data for purchasing patterns, and then use those findings to
customize marketing initiatives so that they’re more exactly targeted for your customer base.