Abstract: Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand.
Abstract: Due to the fast and flawless technological innovation
there is a tremendous amount of data dumping all over the world in
every domain such as Pattern Recognition, Machine Learning, Spatial
Data Mining, Image Analysis, Fraudulent Analysis, World Wide
Web etc., This issue turns to be more essential for developing several
tools for data mining functionalities. The major aim of this paper is to
analyze various tools which are used to build a resourceful analytical
or descriptive model for handling large amount of information more
efficiently and user friendly. In this survey the diverse tools are
illustrated with their extensive technical paradigm, outstanding
graphical interface and inbuilt multipath algorithms in which it is
very useful for handling significant amount of data more indeed.
Abstract: In the present world, predicting rainfall is considered
to be an essential and also a challenging task. Normally, the climate
and rainfall are presumed to have non-linear as well as intricate
phenomena. For predicting accurate rainfall, we necessitate advanced
computer modeling and simulation. When there is an enhanced
understanding of the spatial and temporal distribution of precipitation
then it becomes enrichment to applications such as hydrologic,
climatic and ecological. Conversely, there may be some kind of
challenges occur in the community due to some application which
results in the absence of consistent precipitation observation in
remote and also emerging region. This survey paper provides a
multifarious collection of methodologies which are epitomized by
various researchers for predicting the rainfall. It also gives
information about some technique to forecast rainfall, which is
appropriate to all methods like numerical, traditional and statistical.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: Nowadays, the Web has become one of the most
pervasive platforms for information change and retrieval. It collects
the suitable and perfectly fitting information from websites that one
requires. Data mining is the form of extracting data’s available in the
internet. Web mining is one of the elements of data mining
Technique, which relates to various research communities such as
information recovery, folder managing system and simulated
intellects. In this Paper we have discussed the concepts of Web
mining. We contain generally focused on one of the categories of
Web mining, specifically the Web Content Mining and its various
farm duties. The mining tools are imperative to scanning the many
images, text, and HTML documents and then, the result is used by
the various search engines. We conclude by presenting a comparative
table of these tools based on some pertinent criteria.
Abstract: Over the past era, there have been a lot of efforts and
studies are carried out in growing proficient tools for performing
various tasks in big data. Recently big data have gotten a lot of
publicity for their good reasons. Due to the large and complex
collection of datasets it is difficult to process on traditional data
processing applications. This concern turns to be further mandatory
for producing various tools in big data. Moreover, the main aim of
big data analytics is to utilize the advanced analytic techniques
besides very huge, different datasets which contain diverse sizes from
terabytes to zettabytes and diverse types such as structured or
unstructured and batch or streaming. Big data is useful for data sets
where their size or type is away from the capability of traditional
relational databases for capturing, managing and processing the data
with low-latency. Thus the out coming challenges tend to the
occurrence of powerful big data tools. In this survey, a various
collection of big data tools are illustrated and also compared with the
salient features.
Abstract: Web search engines are designed to retrieve and
extract the information in the web databases and to return dynamic
web pages. The Semantic Web is an extension of the current web in
which it includes semantic content in web pages. The main goal of
semantic web is to promote the quality of the current web by
changing its contents into machine understandable form. Therefore,
the milestone of semantic web is to have semantic level information
in the web. Nowadays, people use different keyword- based search
engines to find the relevant information they need from the web.
But many of the words are polysemous. When these words are
used to query a search engine, it displays the Search Result Records
(SRRs) with different meanings. The SRRs with similar meanings are
grouped together based on Word Sense Disambiguation (WSD). In
addition to that semantic annotation is also performed to improve the
efficiency of search result records. Semantic Annotation is the
process of adding the semantic metadata to web resources. Thus the
grouped SRRs are annotated and generate a summary which
describes the information in SRRs. But the automatic semantic
annotation is a significant challenge in the semantic web. Here
ontology and knowledge based representation are used to annotate
the web pages.
Abstract: An extensive amount of work has been done in data
clustering research under the unsupervised learning technique in Data
Mining during the past two decades. Moreover, several approaches
and methods have been emerged focusing on clustering diverse data
types, features of cluster models and similarity rates of clusters.
However, none of the single clustering algorithm exemplifies its best
nature in extracting efficient clusters. Consequently, in order to
rectify this issue, a new challenging technique called Cluster
Ensemble method was bloomed. This new approach tends to be the
alternative method for the cluster analysis problem. The main
objective of the Cluster Ensemble is to aggregate the diverse
clustering solutions in such a way to attain accuracy and also to
improve the eminence the individual clustering algorithms. Due to
the massive and rapid development of new methods in the globe of
data mining, it is highly mandatory to scrutinize a vital analysis of
existing techniques and the future novelty. This paper shows the
comparative analysis of different cluster ensemble methods along
with their methodologies and salient features. Henceforth this
unambiguous analysis will be very useful for the society of clustering
experts and also helps in deciding the most appropriate one to resolve
the problem in hand.
Abstract: There have been a lot of efforts and researches undertaken in developing efficient tools for performing several tasks in data mining. Due to the massive amount of information embedded in huge data warehouses maintained in several domains, the extraction of meaningful pattern is no longer feasible. This issue turns to be more obligatory for developing several tools in data mining. Furthermore the major aspire of data mining software is to build a resourceful predictive or descriptive model for handling large amount of information more efficiently and user friendly. Data mining mainly contracts with excessive collection of data that inflicts huge rigorous computational constraints. These out coming challenges lead to the emergence of powerful data mining technologies. In this survey a diverse collection of data mining tools are exemplified and also contrasted with the salient features and performance behavior of each tool.
Abstract: Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.
Abstract: Over the past epoch a rampant amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Furthermore several algorithms and methods have been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main hope of the cluster ensemble is to merge different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of new methods in the sphere of data mining and also the incessant interest in inventing new algorithms, makes obligatory to scrutinize a critical analysis of the existing techniques and the future novelty. This paper exposes the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this speculative and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.
Abstract: Routing in MANET is extremely challenging because
of MANETs dynamic features, its limited bandwidth, frequent
topology changes caused by node mobility and power energy
consumption. In order to efficiently transmit data to destinations, the
applicable routing algorithms must be implemented in mobile ad-hoc
networks. Thus we can increase the efficiency of the routing by
satisfying the Quality of Service (QoS) parameters by developing
routing algorithms for MANETs. The algorithms that are inspired by
the principles of natural biological evolution and distributed
collective behavior of social colonies have shown excellence in
dealing with complex optimization problems and are becoming more
popular. This paper presents a survey on few meta-heuristic
algorithms and naturally-inspired algorithms.