Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, reg...

Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and precisely obtain the complete set of high utility item...

The mining process in the Blockchain is very resource intensive, therefore, miners form coalitions to verify each block of transactions in return for a reward where only the first coalition that accomplishes the proof-of-work will be rewarded. This leads to intense competitions among miners and consequently dishonest mining strategies, such as block withholding attack, selfish mining, eclipse a...

In the ever-growing world, concepts of High-utility Itemset Mining (HUIM) as well Frequent (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these only used one factor to estimate an itemset. past, skyline pattern mining by considering both aspects frequency and utility has extensively discussed. most cases, however, people tend foc...

High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most measurements are expected. In real time applications, such as market basket review, company decision making web administration organization results, large quantities datasets slowly evolving with new knowledge incorpora...

The potential employability in different applications has garnered more significance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s value rather than of its periodicity transaction. A MEAN measure added minimum (MIN) and maximum (MAX) incorporate feature into PHUIM this proposed work. MEAN-periodicity bri...

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel nonparametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate...

This paper addresses some fundamental issues related to the foundations of data mining. It is argued that there is an urgent need for formal and mathematical modeling of data mining. A formal framework provides a solid basis for a systematic study of many fundamental issues, such as representations and interpretations of primitive notions of data mining, data mining algorithms, explanations and...

Set-valued data provides enormous opportunities for various data mining tasks. In this paper, we study the problem of publishing set-valued data for data mining tasks under the rigorous differential privacy model. All existing data publishing methods for set-valued data are based on partitionbased privacy models, for example k-anonymity, which are vulnerable to privacy attacks based on backgrou...