In describes the clustering process and overview of

In the year 2011, Han, Jung 265
described the Aspect Oriented Programming (AOP) is well suited to cluster
computing software by using simple, intuitive, and reusable aspects. Throughout
qualitative and performance evaluations, AOP significantly improves the code
readability as well as the modularity, and AOP-based software has the same
performance and scalability as similar software that is developed without using
AOP. Guabtni, Ranjan 266 concerned
with data provisioning services (information search, retrieval, storage, etc.)
deal with a huge and assorted information repository. Increasingly, this class
of benefit is being hosted and delivered through Cloud infrastructures. Awang
268 proposed an algorithm and analytical model based on asynchronous approach
to improve the estimate time, throughput, cleanliness and availability of clustering
in clusters of Web Server. The provision of high authenticity in this model is
by imposing a neighbor intelligent structure on data copies.

 

 Soni, Ganatra 284 provided a categorization of some well known clustering algorithms. It
also describes the clustering process and overview of the different clustering
methods. Bahmani,
Moseley 289 proposed algorithm of initialization k – means   obtains approximately optimal solution after
a mathematical log number of passes, and then visible that in activity a constant
number of passes suffices.

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The
“data mining extensions” (DMX) 2 is a SQL-like language for coding
data-mining models in the Microsoft platform, and therefore it is difficult to
gain understanding of the data-mining domain. Data mining is a highly complex
task which requires a great effort in preprocessing data under analysis, e.g.,
data exploration, cleansing, and integration 9. The 10 provides an entire
framework to carry out data mining but, once again, they are situated at very
low-abstraction level, since they are code-oriented and they do not contribute
to facilitate understanding of the domain problem. Research papers 11 and
12 provide a modeling framework to de ne data-mining techniques at a high-abstraction
level by using UML. However, these UML-based models are mainly used as
documentation. Parsaye 13 examined the relationship between OLAP and data
mining and proposed an architecture integrating OLAP and data mining and
discussed the need for different levels of aggregation for data mining.