Also, it is possible that the k-means algorithm won't find a final solution. In this case it would be a good idea to consider stopping the algorithm after a pre-chosen maximum of
Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal
Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical
Also, they require a threshold to define an appropriate stopping condition for splitting or merging of partitions (Johnson, 1967). Although, they have several advantages such as a better visualization of clusters by generating a tree without predefining the number of clusters but, calculating and sorting the Euclidean distances require a high computational and memory costs. On the other hand, grid-based algorithms have a high efficiency and time complexity is independent of the number of data objects (Yue, Wang, Tao, & Wang, 2010).
Data mining is the process of discovering patterns, trends, correlations from large amounts of data stored electronically in repositories, using statistical methods, mathematical formulas, and pattern recognition technologies (Sharma n.d.). The main idea is to analyze data from different perspectives and discover useful trends, patterns and associations. As discussed in the previous chapter, the healthcare organizations are producing massive amounts of electronic medical records, which are impossible to process using traditional technologies (e.g., Microsoft excel). Therefore data mining is becoming very popular in this field as it can be used to identify the presence of chronic disease, detect the cause of the disease, analyze the effectiveness of treatment methods, predict different medical events, identify the side effects of the drugs, and so on. Kidney diseases such as CKD or AKI require immediate detection and medical attention based on the patient’s clinical condition, medical history, medication history and some demographical factors. From the literature survey, we discovered a good number of studies and tools that used data mining methods such as clustering, association, and classification to improve the decision-making ability of the healthcare providers regarding kidney disease. In the subsequence sections in this chapter, we present an overview of the data mining methods and discuss how they have been used in existing literature.
Data clustering is a method used to group items into different clusters. The items in same cluster are similar and the items in different clusters are dissimilar. Huang (1998) introduced a k-prototypes algorithm that allows for clustering objects with mixed numeric and categorical attributes. The k-prototype algorithm can be used to cluster a large portfolio of the VA contracts with mixed numeric and categorical attributes.
Data mining prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.
Provision of education for each & every student should be the basic initiative for the government in colleges & universities. For higher education many students are short of their tuition fees with popularization of their educational course. In customer segmentation (RFM) i.e. Recency, frequency & monetary method plays an important role. The prime goal in this case study is to build customer segmentation RFM model in a university for needy students through dining room database. After collecting the database this study can be applied using K-means algorithm to identify students. Through case study, the needy students list can be generated & can be provided to the department of university as a reference.
2. Detection using factors: The pre- duplicate record elimination stage is useful for removing data but it helps in retaining only one copy of the duplicate data and removing the rest. For this purpose, a threshold value is calculated for all the records and a similarity. Threshold value is calculated for elimination purpose. All the possible pairs are selected from the clusters
In the case of Image Recognition the concept of clustering can be applied to identify the clusters in handwritten character recognition systems. Many applications of clustering are also found in Web search. Clustering can be utilized to organize the query results in groups and present the outcomes in a concise and effectively available way. We can distinguish and sparse regions in object space by automated clustering and from that we can find general interesting correlations and overall distribution patterns among data attributes. Cluster analysis has been broadly utilized as a part of various applications, like market research, pattern recognition, data analysis, and image processing. In business, clustering can offer marketers some assistance with discovering distinct groups in their client bases and portray client groups taking into account the purchasing patterns. In science, it can be utilized to determine plant and animal scientific categorizations, order qualities with similar functionality, and addition knowledge into structures inborn in populations. For the identification of the same land use in the earth observation database we use clustering. It can also be used for finding groups of houses in a city based on the house type, value and the geographic location. It will be helpful in identifying the groups of the policy holders with the highest average claim cost of the automobile insurance.
Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian and Lazy classifiers for hepatitis dataset. In Bayesian classifier there are two algorithms namely BayesNet and NaiveBayes. In Lazy classifier we have two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool.It is open source software which consists of the collection of machine learning algorithms for data mining tasks.
In data mining, inductive learning techniques are used when constructing a model which ensures that trained data model can be applied for future cases.
Clustering- It involves identifying clusters and grouping similar objects together in each cluster. The main focus is on evaluating and implementing Partitioned (K-means) algorithms, other clustering methods include Hierarchical (CURE, BIRCH), Grid – based (STING, WaveCluster), Model-based (Cobweb), and Density based (DBSCAN). Author  presented work to enhance the performance of one of the most well-known pop
The proposed model performs the segmentation process using clustering method which is considered an efficient segmentation technique. Clustering is the process of classifies objects into different groups and classes. In clustering the data is classified according to a common features shared between the object and the target class. The process of portioning data into a number of subsets is often referred to as supervised learning or clustering. K-means is one of clustering techniques designed and used in many approaches .
Abstract— The set of objects having same characteristics are organized in groups and clusters of these objects are formed known as Data Clustering.It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters.In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization,cluster analysis,feature learning are some of the application of K-Means.However results generated using this algorithm are mainly dependant on choosing initial cluster centroids.The main shortcome of this algorithm is to provide appropriate number of clusters.Provision of number of clusters before applying the algorithm is highly impractical and requires deep knowledge of clustering