Abstract: Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.
Abstract: This paper presents an approach for easy creation and
classification of institutional risk profiles supporting endangerment
analysis of file formats. The main contribution of this work is the
employment of data mining techniques to support set up of the most
important risk factors. Subsequently, risk profiles employ risk factors
classifier and associated configurations to support digital preservation
experts with a semi-automatic estimation of endangerment group
for file format risk profiles. Our goal is to make use of an expert
knowledge base, accuired through a digital preservation survey
in order to detect preservation risks for a particular institution.
Another contribution is support for visualisation of risk factors for
a requried dimension for analysis. Using the naive Bayes method,
the decision support system recommends to an expert the matching
risk profile group for the previously selected institutional risk profile.
The proposed methods improve the visibility of risk factor values
and the quality of a digital preservation process. The presented
approach is designed to facilitate decision making for the preservation
of digital content in libraries and archives using domain expert
knowledge and values of file format risk profiles. To facilitate
decision-making, the aggregated information about the risk factors
is presented as a multidimensional vector. The goal is to visualise
particular dimensions of this vector for analysis by an expert and
to define its profile group. The sample risk profile calculation and
the visualisation of some risk factor dimensions is presented in the
evaluation section.
Abstract: Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.
Abstract: This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.
Abstract: The biological function of an RNA molecule depends
on its structure. The objective of the alignment is finding the
homology between two or more RNA secondary structures. Knowing
the common functionalities between two RNA structures allows
a better understanding and a discovery of other relationships
between them. Besides, identifying non-coding RNAs -that is not
translated into a protein- is a popular application in which RNA
structural alignment is the first step A few methods for RNA
structure-to-structure alignment have been developed. Most of these
methods are partial structure-to-structure, sequence-to-structure, or
structure-to-sequence alignment. Less attention is given in the
literature to the use of efficient RNA structure representation and the
structure-to-structure alignment methods are lacking. In this paper,
we introduce an O(N2) Component-based Pairwise RNA Structure
Alignment (CompPSA) algorithm, where structures are given as
a component-based representation and where N is the maximum
number of components in the two structures. The proposed algorithm
compares the two RNA secondary structures based on their weighted
component features rather than on their base-pair details. Extensive
experiments are conducted illustrating the efficiency of the CompPSA
algorithm when compared to other approaches and on different real
and simulated datasets. The CompPSA algorithm shows an accurate
similarity measure between components. The algorithm gives the
flexibility for the user to align the two RNA structures based on
their weighted features (position, full length, and/or stem length).
Moreover, the algorithm proves scalability and efficiency in time and
memory performance.
Abstract: Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.
Abstract: In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.
Abstract: One image is worth more than thousand words.
Images if analyzed can reveal useful information. Low level image
processing deals with the extraction of specific feature from a single
image. Now the question arises: What technique should be used to
extract patterns of very large and detailed image database? The
answer of the question is: “Image Mining”. Image Mining deals with
the extraction of image data relationship, implicit knowledge, and
another pattern from the collection of images or image database. It is
nothing but the extension of Data Mining. In the following paper, not
only we are going to scrutinize the current techniques of image
mining but also present a new technique for mining images using
Genetic Algorithm.
Abstract: Existing methods of data mining cannot be applied on
spatial data because they require spatial specificity consideration, as
spatial relationships.
This paper focuses on the classification with decision trees, which
are one of the data mining techniques. We propose an extension of
the C4.5 algorithm for spatial data, based on two different approaches
Join materialization and Querying on the fly the different tables.
Similar works have been done on these two main approaches, the
first - Join materialization - favors the processing time in spite of
memory space, whereas the second - Querying on the fly different
tables- promotes memory space despite of the processing time.
The modified C4.5 algorithm requires three entries tables: a target
table, a neighbor table, and a spatial index join that contains the
possible spatial relationship among the objects in the target table and
those in the neighbor table. Thus, the proposed algorithms are applied
to a spatial data pattern in the accidentology domain.
A comparative study of our approach with other works of
classification by spatial decision trees will be detailed.
Abstract: GRF, Growth regulating factor, genes encode a novel
class of plant-specific transcription factors. The GRF proteins play a
role in the regulation of cell numbers in young and growing tissues
and may act as transcription activations in growth and development
of plants. Identification of GRF genes and their expression are
important in plants to performance of the growth and development of
various organs. In this study, to better understanding the structural
and functional differences of GRFs family, 45 GRF proteins
sequences in A. thaliana, Z. mays, O. sativa, B. napus, B. rapa, H.
vulgare and S. bicolor, have been collected and analyzed through
bioinformatics data mining. As a result, in secondary structure of
GRFs, the number of alpha helices was more than beta sheets and in
all of them QLQ domains were completely in the biggest alpha helix.
In all GRFs, QLQ and WRC domains were completely protected
except in AtGRF9. These proteins have no trans-membrane domain
and due to have nuclear localization signals act in nuclear and they
are component of unstable proteins in the test tube.
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: The high utilization rate of Automated Teller Machine (ATM) has inevitably caused the phenomena of waiting for a long time in the queue. This in turn has increased the out of stock situations. The ATM utilization helps to determine the usage level and states the necessity of the ATM based on the utilization of the ATM system. The time in which the ATM used more frequently (peak time) and based on the predicted solution the necessary actions are taken by the bank management. The analysis can be done by using the concept of Data Mining and the major part are analyzed based on the predictive data mining. The results are predicted from the historical data (past data) and track the relevant solution which is required. Weka tool is used for the analysis of data based on predictive data mining.
Abstract: The application of data mining to environmental monitoring has become crucial for a number of tasks related to emergency management. Over recent years, many tools have been developed for decision support system (DSS) for emergency management. In this article a graphical user interface (GUI) for environmental monitoring system is presented. This interface allows accomplishing (i) data collection and observation and (ii) extraction for data mining. This tool may be the basis for future development along the line of the open source software paradigm.
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: The most important subtype of non-Hodgkin-s
lymphoma is the Diffuse Large B-Cell Lymphoma. Approximately
40% of the patients suffering from it respond well to therapy,
whereas the remainder needs a more aggressive treatment, in order to
better their chances of survival. Data Mining techniques have helped
to identify the class of the lymphoma in an efficient manner. Despite
that, thousands of genes should be processed to obtain the results.
This paper presents a comparison of the use of various attribute
selection methods aiming to reduce the number of genes to be
searched, looking for a more effective procedure as a whole.
Abstract: In data mining, the association rules are used to search
for the relations of items of the transactions database. Following the
data is collected and stored, it can find rules of value through
association rules, and assist manager to proceed marketing strategy
and plan market framework. In this paper, we attempt fuzzy partition
methods and decide membership function of quantitative values of
each transaction item. Also, by managers we can reflect the
importance of items as linguistic terms, which are transformed as
fuzzy sets of weights. Next, fuzzy weighted frequent pattern growth
(FWFP-Growth) is used to complete the process of data mining. The
method above is expected to improve Apriori algorithm for its better
efficiency of the whole association rules. An example is given to
clearly illustrate the proposed approach.
Abstract: Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.
Abstract: Decision tree algorithms have very important place at
classification model of data mining. In literature, algorithms use
entropy concept or gini index to form the tree. The shape of the
classes and their closeness to each other some of the factors that
affect the performance of the algorithm. In this paper we introduce a
new decision tree algorithm which employs data (attribute) folding
method and variation of the class variables over the branches to be
created. A comparative performance analysis has been held between
the proposed algorithm and C4.5.
Abstract: A clustering is process to identify a homogeneous
groups of object called as cluster. Clustering is one interesting topic
on data mining. A group or class behaves similarly characteristics.
This paper discusses a robust clustering process for data images with
two reduction dimension approaches; i.e. the two dimensional
principal component analysis (2DPCA) and principal component
analysis (PCA). A standard approach to overcome this problem is
dimension reduction, which transforms a high-dimensional data into
a lower-dimensional space with limited loss of information. One of
the most common forms of dimensionality reduction is the principal
components analysis (PCA). The 2DPCA is often called a variant of
principal component (PCA), the image matrices were directly treated
as 2D matrices; they do not need to be transformed into a vector so
that the covariance matrix of image can be constructed directly using
the original image matrices. The decomposed classical covariance
matrix is very sensitive to outlying observations. The objective of
paper is to compare the performance of robust minimizing vector
variance (MVV) in the two dimensional projection PCA (2DPCA)
and the PCA for clustering on an arbitrary data image when outliers
are hiden in the data set. The simulation aspects of robustness and
the illustration of clustering images are discussed in the end of
paper
Abstract: Biclustering is a very useful data mining technique for
identifying patterns where different genes are co-related based on a
subset of conditions in gene expression analysis. Association rules
mining is an efficient approach to achieve biclustering as in
BIMODULE algorithm but it is sensitive to the value given to its
input parameters and the discretization procedure used in the
preprocessing step, also when noise is present, classical association
rules miners discover multiple small fragments of the true bicluster,
but miss the true bicluster itself. This paper formally presents a
generalized noise tolerant bicluster model, termed as μBicluster. An
iterative algorithm termed as BIDENS based on the proposed model
is introduced that can discover a set of k possibly overlapping
biclusters simultaneously. Our model uses a more flexible method to
partition the dimensions to preserve meaningful and significant
biclusters. The proposed algorithm allows discovering biclusters that
hard to be discovered by BIMODULE. Experimental study on yeast,
human gene expression data and several artificial datasets shows that
our algorithm offers substantial improvements over several
previously proposed biclustering algorithms.