BigFCM: Fast, Precise and Scalable FCM on Hadoop

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Fuzzy Bayesian Belief Network for Analyzing Medical Track Record

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, the BBN concept is extended with meaningful fuzzy labels for mining fuzzy association rules. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels secondary disease and complication disease are defined for disease classification. We extend the concept of Mutual Information dealing with fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining association among nodes. A brief algorithm is introduced to develop the proposed concept. Experimental results of the algorithm show processing time in the relation to the number of records and the number of nodes. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area

Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.fuzzy clustering;direct marketing;client segmentation;fuzzy systems

Data Mining and Fuzzy Data Mining Using MapReduce Algorithms

Data mining is knowledge discovery process. It has to deal with exact information and inexact information. Statistical methods deal with inexact information but it is based on likelihood. Zadeh fuzzy logic deals with inexact information but it is based on belief and it is simple to use. Fuzzy logic is used to deal with inexact information. Data mining consist methods and classifications. These methods and classifications are discussed for both exact and inexact information. Retrieval of information is important in data mining. The time and space complexity is high in big data. These are to be reduced. The time complexity is reduced through the consecutive retrieval (C-R) property and space complexity is reduced with blackboard systems. Data mining for web data based is discussed. In web data mining, the original data have to be disclosed. Fuzzy web data mining is discussed for security of data. Fuzzy web programming is discussed. Data mining, fuzzy data mining, and web data mining are discussed through MapReduce algorithms

Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process