Data Clustering

Improving student's academic performance is not an easy task for the academic community of higher learning. The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average (GPA) in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment lab - work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. In this paper, we present a hybrid procedure based on Decision Tree of Data mining method and Data Clustering that enables academicians to predict student's GPA and based on that instructor can take necessary step to improve student academic performance Graded Point Average (GPA) is a co...

Abstract: The current data tends to be more complex than conventional data and need dimension reduction. Dimension reduction is important in cluster analysis and creates a smaller data in volume and has the same analytical results as the original representation. A clustering process needs data reduction to obtain an efficient processing time while clustering and mitigate curse of dimensionality. This paper proposes a model for extracting multidimensional data clustering of health database. We implemented four dimension ...

Clustering plays an outstanding role in data mining research. Among the various algorithms for clustering, most of the researchers used the Fuzzy C-Means algorithm (FCM) in the areas like computational geometry, data compression and vector quantization, pattern recognition and pattern classification. In this research, a simple and efficient implementation of FCM clustering algorithm is presented. Three types of inputs are given to algorithm. The data points are first distributed manually, the statistical distributions Normal and Uniform are anther two methods by using the Box-Muller formula. The algorithm is analyzed based on their clustering quality. The behavior of the algorithm depends on the number of data points as well as on the number of cluster. The performance of the algorithm is investigated during different execution of the program on the input data points. The execution time for each cluster and total elapsed time to cluster all the data points is also analyzed and the r...

Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields, where there is a need to learn the inherent grouping structure of data in an unsupervised manner. There are many clustering approaches proposed in the literature with different quality/complexity tradeoffs. Each clustering algorithm works on its domain space with no optimum solution for all datasets of different properties, sizes, structures, and distributions. In this paper, a novel cooperative clustering (CC) model is presented. It involves cooperation among multiple clustering techniques for the goal of increasing the homogeneity of objects within the clusters. The CC model is capable of handling datasets with different properties by developing two data structures, a histogram representation of the pair-wise similarities and a cooperative contingency graph. The two data structures are designed to find the matching sub-clusters between different clusterings and to obtain the final set of clusters through a coherent merging process. The cooperative model is consistent and scalable in terms of the number of adopted clustering approaches. Experimental results show that the cooperative clustering model outperforms the individual clustering algorithms over a number of gene expression and text documents datasets.

Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users. The cloud addresses three main areas of operation SaaS (software-as-a-service),PaaS (platform-as-a-service) and IaaS (infrastructure as a service). The large amount of data can be stored into cloud Data centers with low cost. We Integrate Data Mining and Cloud Computing to provide a quick access to this large volume amount of data on cloud. This paper aimed to study Clustering algorithm which can be applicable in cloud computing. This paper also describes the role of soft clustering in Cloud computing environment.

E-auctions have attracted serious fraud, such as Shill Bidding (SB), due to the large amount of money involved and anonymity of users. SB is difficult to detect given its similarity to normal bidding behavior. To this end, we develop an efficient SVM-based fraud classifier that enables auction companies to distinguish between legitimate and shill bidders. We introduce a robust approach to build offline the optimal SB classifier. To produce SB training data, we combine the hierarchical clustering and our own labelling strategy, and then utilize a hybrid data sampling method to solve the issue of highly imbalanced SB datasets. To avert financial loss in new auctions, the SB classifier is to be launched at the end of the bidding period and before auction finalization. Based on commercial auction data, we conduct experiments for offline and online SB detection. The classification results exhibit good detection accuracy and mis-classification rate of shill bidders.

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.

The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach (EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research. MOEAs have substantial success across a variety of MOP applications, from pedagogical multifunction optimization to real-world engineering design. The survey paper noticeably organizes the developments witnessed in the past three decades for EAs based metaheuristics to solve multiobjective optimization problems (MOP) and to derive significant progression in ruling high quality elucidations in a single run.
Data clustering is an exigent task, whose intricacy is caused by a lack of unique and precise definition of a cluster. The discrete optimization problem uses the cluster space to derive a solution for Multiobjective data clustering. Discovery of a majority or all of the clusters (of illogical shapes) present in the data is a long-standing goal of unsupervised predictive learning problems or exploratory pattern analysis.

Shill Bidding (SB) has been recognized as the predominant online auction fraud and also the most difficult to detect due to its similarity to normal bidding behavior. Previously, we produced a high-quality SB dataset based on actual auctions and effectively labeled the instances into normal or suspicious. To overcome the serious problem of imbalanced SB datasets, in this study, we investigate over-and under-sampling techniques through several instance-based classification algorithms. Thousands of auctions occur in eBay every day, and auction data may be sent continuously to the optimal fraud classifier to detect potential SB activities. Consequently , instance-based classification is appropriate for our particular fraud detection problem. According to the experimental results, incremental classification returns high performance for both over-and under-sampled SB datasets. Still, over-sampling slightly outperforms under-sampling for both normal and suspicious classes across all the classifiers.

Binary Data clustering finds tremendous
applications in fault analysis of machineries, document
classification, image retrievals and analysis, medical diagnosis of
diseases etc. Accurate clustering of binary databases provides
numerous help to develop accurate designs of above systems. In
this manuscript a density based clustering algorithm is proposed
to effectively cluster binary datasets. The proposed algorithm
automatically determines the number of clusters based upon the
density of data present in a region. The number of clusters evolve
during the clustering process due to merging of several smaller
clusters. Simulation studies were carried out on two synthetic
datasets and it is observed that the proposed algorithm can
effectively clusters both correlated and random binary datasets.
K􀀮words-Density based clustering, DBSCAN, Binary Datasets,
Similarity Measure.

This paper describes an experiment performed using different approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in this context, and from the hierarchical clustering algorithm HACC-Spatial, especially designed for this PA task. We also performed experiments using traditional ensembles approaches from the literature, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from features splitting or running one of the abovementioned algorithms. Results showed some differences between FCM and HACC-Spatial, mainly for the visu-alization of management classes in the form of maps. Considering the consensus clusterings provided by ensembles, it became clear the attempt to achieve an agreement result that most closely matches the original clusterings, showing us some details that may go undetected when we analyse only the individual clusterings.

A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which suggest usage of different resources of the server. Feature selection algorithms have been used to extract the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using an esxtop command. Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index respectively. The best cluster is further identified by the determined indices. The features of the best cluster are considered into a set of optimal parameters.

Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.

Data Clustering is a descriptive data mining task of finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups [5]. The motivation behind this research paper is to explore KMeans partitioning algorithm in the currently available parallel architecture using parallel programming models. Parallel KMeans algorithms have been implemented for a shared memory model using OpenMP programming and distributed memory model using MPI programming. A hybrid version of OpenMP in MPI programming also has been experimented. The performance of the parallel algorithms were analysed to compare the speedup obtained and to study the Amdhals effect. The computational time of hybrid method was reduced by 50% compared to MPI and was also more efficient with balanced load.

This paper describes a vision-based system for blind spot detection in intelligent vehicle applications. A camera is mounted in the lateral mirror of a car with the intention of visually detecting cars that can not be perceived by the vehicle driver since they are located in the so-called blind spot. The detection of cars in the blind spot is carried out using computer vision techniques, based on optical flow and data clustering, as described in the following lines.

Many scientific applications can benejit from eficient clustering algorithm of massively large high dimensional datasets. However most of the developed ,algorithms are impractical to use when the amount of data is very large. Given N objects each de3ned by an M-dimensional fea- ture vectol; any clustering technique for handling very large datasets in high dimensional space should run in time

In this paper we show apply text mining techniques, Correspondence Analysis and Fuzzy C-Means Clustering in order to identify associations among countries and titles of documents available at a profile in Academia.edu. All analysis was carried out by R software and several R scripts are presented.

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-ICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. To make use of this information, in this paper, we develop a new clustering method “MLP-KMEANS” by combining Multi-Layer Perceptron and K-means. We test our method on several data sets with partial constrains available. Experimental results show that our method can effectively improve clustering accuracy by utilizing available information.

"Clustering is said to be one of the most complex, well-known and most studied problems in data mining theory. Data clustering is the process of grouping the data into classes or clusters, so that objects within a cluster have high similarity in comparison to one another but are very dissimilar to objects in other clusters. The increasing enrollment of students at Batangas State University (BatStateU) equates to increase of students’ database which can be mined to discover patterns in large data sets. Patterns extracted can be converted to understandable information that can be useful to the organization. A popular data clustering algorithm known as Clustering Using Representative (CURE) was implemented using C# programming language to cluster the students’ database of Batangas State University."

Anomaly detection refers to methods that provide warnings of unusual behaviors which may compromise the security and performance of communication networks. In this paper it is proposed a novel model for network anomaly detection combining baseline, K-means clustering and particle swarm optimization (PSO). The baseline consists of network traffic normal behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set, while K-means is a supervised learning clustering algorithm used to recognize patterns or features in data sets. In order to escape from local optima problem, the K-means is associated to PSO, which is a meta-heuristic whose main characteristics include low computational complexity and small number of input parameters dependence. The proposed anomaly detection approach classifies data clusters from baseline and real traffic using the K-means combined with PSO. Anomalous behaviors can be identified by comparing the distance between real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.