Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

International Journal of Computer and Applications

A comprehensive survey for scheduling techniques in cloud computing

There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

In recent years, the advent of latest web and data technologies has encouraged massive data growth in almost every sector. Businesses and leading industries are viewing these huge data repositories as a tool to design future strategies, prediction models by analyzing patterns and gaining knowledge from this unstructured data by applying different data mining techniques. Medical domain has now become richer in term of maintaining digital records of patients related to their diagnosis and treatment. These huge data repositories can range from patient personnel data, diagnosis, treatment histories, test diagnosis, images and various scans. This terabytes of medical data is quantity rich but weaker in information in terms of knowledge and robust tools to identify hidden patterns of knowledge specifically in medical sector. Data Mining as a field of research has already well proven capabilities of identifying hidden patterns, analysis and knowledge applied on different research domains, now gaining popularity day by day among researchers and scientist towards generating novel and deep insights of these large biomedical datasets also. Uncovering new biomedical and healthcare related knowledge in order to support clinical decision making, is another dimension of data mining. Through massive literature survey, it is found that early disease prediction is the most demanded area of research in health care sector. As health care domain is bit wider domain and having different disease characteristics, different techniques have their own prediction efficiencies, which can be enhanced and changed in order to get into most optimize way. In this research work, authors have comprehensively compared different data classification techniques and their prediction accuracy for chronic kidney disease. Authors have compared J48, Naive Bayes, Random Forest, SVM and k-NN classifiers using performance measures like ROC, kappa statistics, RMSE and MAE using WEKA tool. Authors have also compared these classifiers on various accuracy measures like TP rate, FP rate, precision, recall and f-measure by implementing on WEKA. Experimental result shows that random forest classifier has better classification accuracy over others for chronic kidney disease dataset.

Implementing WEKA for medical data classification and early disease prediction

A Preference-based Resource Allocation in Cloud Computing Systems

A novel intrusion detection system using hybrid clustering-optimization approach in cloud computing

Migration Performance of Cloud Applications- A Quantitative Analysis☆

To find the unknown and hidden pattern from large amount of data of insurance organizations. There are strong customer base required with the help of large database. Cluster Analysis is an excellent statistical tool for a large and multivariate database. The clusters analysis with K-Means method may be used to develop the model which is useful to find the relationship in a database. In this paper, consider the data of LIC customer, the seeds are the first three customers then compute the distance from cluster using the attributes of customers with the help of Clustering with K-Means method. Comparing the mean distance of cluster with the seeds. Finally, we find the nigh distances from the cluster as the cluster (C1) have three customers named S1, S2, S10 which are satisfy with all the benefits, terms and conditions of cluster (C1). If requirements of any customer same as the S1, S2, S10 then we allocated the cluster (C1). It will increase the revenue as well as profit of the organization with customer satisfaction.

/pdf/cluster-analysis-in-data-mining-using-k-means-method-4gxfle4vbh.pdf

Narander Kumar

Papers

Implementing WEKA for medical data classification and early disease prediction

A Preference-based Resource Allocation in Cloud Computing Systems

A novel intrusion detection system using hybrid clustering-optimization approach in cloud computing

Migration Performance of Cloud Applications- A Quantitative Analysis☆

Cluster analysis in data mining using k-means method