Notes

Typical Requirements Of Clustering InData Mining:
 Scalability:
Many clustering algorithms work well on small data sets containing fewer than several
hundred data objects; however, a large database may contain millions of objects. Clustering
on a sample of a given large data set may lead to biased results.
Highly scalable clustering algorithms are needed.

 Ability to deal with different types of attributes:
Many algorithms are designed to cluster interval-based (numerical) data. However,
applications may require clustering other types of data, such as binary, categorical
(nominal), and ordinal data, or mixtures of these data types.

 To determine minimal input parameters:
Many clustering algorithms require users to input certain parameters in cluster analysis
(such as the number of desired clusters). The clustering results can be quite sensitive to
input parameters. Parameters are often difficult to determine, especially for data sets
containing high-dimensional objects. This not only burdens users, but it also makes the
quality of clustering difficult to control.

 Ability to deal with noisy data:
Most real-world databases contain outliers or missing, unknown, or erroneous data.
Some clustering algorithms are sensitive to such data and may lead to clusters of poor
quality.

 Interpretability and usability:
Users expect clustering results to be interpretable, comprehensible, and usable. That is,
clustering may need to be tied to specific semantic interpretations and applications. It is
important to study how an application goal may influence the selection of clustering
features and methods.

 Incremental clustering and insensitivity to the order of input records:
Some clustering algorithms cannot incorporate newly inserted data (i.e., database updates)
into existing clustering structures and, instead, must determine a new clustering from
scratch. Some clustering algorithms are sensitive to the order of input data.
That is, given a set of data objects, such an algorithm may return dramatically different
clusterings depending on the order of presentation of the input objects.
It is important to develop incremental clustering algorithms and algorithms thatare
insensitive to the order of input.

 High dimensionality:
A database or a data warehouse can contain several dimensionsor attributes.Many
clustering algorithms are good at handling low-dimensional data,involving only two to
three dimensions. Human eyes are good at judging the qualityof clustering for up to three
dimensions. Finding clusters of data objects in highdimensionalspace is challenging,
especially considering that such data can be sparseand highly skewed.