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You can see there are many similarities between data mining and machine learning.
For classification and clustering tasks, machine learning research often focuses on the
accuracy of the model. In addition to accuracy, data mining research places strong
emphasis on the efficiency and scalability of mining methods on large data sets, as well
as on ways to handle complex types of data and explore new, alternative methods.

1.5.3 Database Systems and Data Warehouses
Database systems research focuses on the creation, maintenance, and use of databases
for organizations and end-users. Particularly, database systems researchers have estab-
lished highly recognized principles in data models, query languages, query processing
and optimization methods, data storage, and indexing and accessing methods. Database
systems are often well known for their high scalability in processing very large, relatively
structured data sets.
Many data mining tasks need to handle large data sets or even real-time, fast stream-
ing data. Therefore, data mining can make good use of scalable database technologies to
achieve high efficiency and scalability on large data sets. Moreover, data mining tasks can
be used to extend the capability of existing database systems to satisfy advanced users’
sophisticated data analysis requirements.
Recent database systems have built systematic data analysis capabilities on database
data using data warehousing and data mining facilities. A data warehouse integrates
data originating from multiple sources and various timeframes. It consolidates data
in multidimensional space to form partially materialized data cubes. The data cube
model not only facilitates OLAP in multidimensional databases but also promotes
multidimensional data mining (see Section 1.3.2).

1.5.4 Information Retrieval
Information retrieval (IR) is the science of searching for documents or information
in documents. Documents can be text or multimedia, and may reside on the Web. The
differences between traditional information retrieval and database systems are twofold:
Information retrieval assumes that (1) the data under search are unstructured; and (2)
the queries are formed mainly by keywords, which do not have complex structures
(unlike SQL queries in database systems).
The typical approaches in information retrieval adopt probabilistic models. For
example, a text document can be regarded as a bag of words, that is, a multiset of words
appearing in the document. The document’s language model is the probability density
function that generates the bag of words in the document. The similarity between two
documents can be measured by the similarity between their corresponding language
models.
Furthermore, a topic in a set of text documents can be modeled as a probability dis-
tribution over the vocabulary, which is called a topic model. A text document, which
may involve one or multiple topics, can be regarded as a mixture of multiple topic mod-
els. By integrating information retrieval models and data mining techniques, we can find