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 feedback time, throughput, purity and availability of clustering
in Web Server Cluster. The provision of high reliability in this model is by
imposing a neighbor logical structure on data copies. Data from one server will
be replicated to its neighboring server and vice versa in the face of failures. 

 

 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.

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.