Ritu Chauhan

Data mining cluster analysis on the influence of health factors in Casemix data.

Background This study explores potential data mining applications in the Casemix context, which is expected to yield effective and efficient health care services. The objective of work focuses on determining hidden relevant patterns which can’t be processed by human capabilities all alone. California Drug and Alcohol treatment Assessment (CALDATA) of administrative type database can be relevant study for the medical diagnosis in usage of alcohol and drugs for patients admitted and discharged during the stay in hospital to discover knowledge for recovery process.

SpaGRID: a spatial grid framework for high dimensional medical databases

The outgrowth of technology in geographical databases has enhanced the growth of spatial databases, to deal with such enlarging databases scientists are laying down enormous efforts that can efficiently process these databases. Spatial data mining techniques has been collaboratively applied to extract implicit knowledge from spatial as well as non-spatial attributes. These techniques are efficiently applied in several fields such as healthcare, environmental, marketing and remote sensing databases to improve planning and decision making process. In this paper, we have designed and implemented SpaGRID framework for detection of spatial clusters. The framework has unprecedented efficiency to extract implicit knowledge of spatial data, due to its accessibility to handle and discover hidden patterns from spatial databases. We have also illustrated the usage of spatial variations among the United States men with prevalence of prostate cancer disease. The data of age group was taken from (15-65+) years in this group prostate cancers were examined and several stages of disease diagnosis was taken into account. The population of data was characterized by white, black and others were too small to be taken into account. Numerous challenges were encountered due to complexity of spatial datasets hence being resolved by certain statistical measures. The approach is to discover knowledge from spatial databases and design different aspects of knowledge discovery process from spatial databases.

An Optimal Categorization of Feature Selection Methods for Knowledge Discovery

With the continuous availability of massive experimental medical data has given impetus to a large effort in developing mathematical, statistical and computational intelligent techniques to infer models from medical databases. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. However, there have been relatively few studies on preprocessing data used as input for data mining systems in medical data. In this chapter, the authors focus on several feature selection methods as to their effectiveness in preprocessing input medical data. They evaluate several feature selection algorithms such as Mutual Information Feature Selection (MIFS), Fast Correlation-Based Filter (FCBF) and Stepwise Discriminant Analysis (STEPDISC) with machine learning algorithm naive Bayesian and Linear Discriminant analysis techniques. The experimental analysis of feature selection technique in medical databases has enable the authors to find small number of informative features leading to potential improvement in medical diagnosis by reducing the size of data set, eliminating irrelevant features, and decreasing the processing time. DOI: 10.4018/978-1-4666-2455-9.ch005

An Enhancement in Service Broker Policy for Cloud-Analyst

Cloud computing means storing and accessing data and programs over the Internet instead of one’s personal computers hard drive. The cloud is an analogy for the Internet. Now a day, it becomes very popular due to new trend and technology. It deals with large amount of data, so, it is necessary to simulate the behavior of cloud in real time environment. For this purpose, the simulation tool such as, cloud-analyst, cloudSim, are commonly used, which has been provided by laboratory CLOUD. The simulator cloud analyst uses different load balancing policy, service broker strategy with different parameters and has used as per requirement. This paper presents an improvement in service broker strategy, which enhances the performance of data center.

A feature-based selection technique for reduction of large scale data

The inflated development in public healthcare domain has forced numerous organisations to construct and maintain large scale databases or data warehouses. However, the prediction of knowledge should be an automated process to discover hidden information from large scale databases. The elaborated studies in the past suggest that minimum interesting variables can determine qualified information while preserving information among the data. In addition, it is determined that large scale databases usually comprise of redundant and irrelevant features which have proven to be a major setback for efficient and effective analysis of data. This paper intends to provide an integrated approach by utilising machine learning technique and other convention statistical techniques for extraction of information from large scale databases. In the formulated approach, we have potentially exploited two approaches where the first approach emphasises on retrieval of feature subsets using MODTree filtering technique from discretised datasets with relative application domain on real datasets of Substance Abuse and Mental Health Data Archive (SAMHDA) collected from different states of USA. The second phase of study exploits statistical techniques on potential targets for discovery of interesting information from reduced datasets. We present a novel perspective using feature selection and statistical techniques for determination of knowledge from large scale databases.

Transport Control Protocol for Cognitive Radio Ad Hoc Networks

The rapid demand for wireless communication networks in the last few decades has lead to spectrum scarcity, Hence there is need of efficient spectrum utilization is occurs. Cognitive radio is a novel technology which improve the spectrum resource utilization and allows a cognitive radio transceiver to detect and sense the available spectrum. Cognitive Radio (CR) networks allow users to transmit in the licensed spectrum bands, without degrading the performance of the Primary Users (PUs). Routing in CRN is a challenging task due to the variations in spectrum availability with time and periodic spectrum sensing undertaken by the CR users. In this paper we investigate the effect of TCP in CR ad hoc network environment and proposes multipath routing protocol, that uses on-demand distance vector routing, called Ad hoc On-demand Multipath Distance Vector (AOMDV) Routing Protocol. We analyze its performance using Network simulator (NS-2) software tool. Simulation gives effective result of cognitive radio ad hoc networks and analyzed the parameter delay, throughput and energy.

A Knowledge Driven Model: Extract Knowledge from High Dimensional Medical Databases

The extensive amount of spatial databases accumulated from various computed technology requires automated data mining tools to discover hidden and novel information from large and complex databases. In other words, the major concern is high dimensionality and complexity of spatial data which has created serious concerns among the researchers to retrieve effective and efficient clusters from large and complex spatial features. In this paper we have proposed a spatial clustering algorithm (SPAS) and Knowledge driven framework to discover clusters of variant shapes and size with domain specific knowledge. The application of our proposed algorithm is tested on real world spatial medical databases collected from SEER datasets which has record of Lung cancer patients from the year 1975 - 2008. The case includes information on patients gender, ZIP code of a patients residence, year of diagnosis, primary site, stage at diagnosis, and age group. Each record represents a diagnosed cancer case assigned to the patients residence at time of diagnosis. The objective of study was to discover effective and efficient spatial clusters with domain specific knowledge for futuristic decision making.