Murlidher Mourya

Abstract- Mining of frequent item sets is one of the most fundamental problems in data mining applications. My proposed algorithm which guides the seller to select the best attributes of a new product to be inserted in the database so that it stands out in the existing competitive products, due to budget constraints there is a limit, say m, on the number of attribute that can be selected for the entry into the database. Although the problems are NP complete. The Approximation algorithm are based on greedy heuristics. My proposed algorithm performs effectively and generates the frequent item sets faster. Index Terms- Association rules,Data mining, Mining frequent itemsets. I I.

— Mining of the Data now days plays a major role and concern in the present world in the industry and also in the research areas. Many tools and application software’s are evolved to mine the data and to present the best data visualization. One of the Data Mining tool available is WEKA, in which contain many machine level and other types of algorithms. In the present paper the data classification is a medical dataset of diabetes category in which we cluster the dataset using various clustering algorithms like EM, k-means, OPTICS and the results are depicted.

A wireless sensor network can get separated into multiple connected components due to the failure of some of its nodes, which is called a "cut". In this article we consider the problem of detecting cuts by the remaining nodes of a wireless sensor network. We propose an algorithm that allows (i) every node to detect when the connectivity to a specially designated node has been lost, and (ii) one or more nodes (that are connected to the special node after the cut) to detect the occurrence of the cut. The algorithm is distributed and asynchronous: every node needs to communicate with only those nodes that are within its communication range. The algorithm is based on the iterative computation of a fictitious "electrical potential" of the nodes. The convergence rate of the underlying iterative scheme is independent of the size and structure of the network.

Association rule mining is the power ful tool now a days in Data mining. It identifies the correlation between the items in large databases. A typical example of Association rule mining is Market Basket analysis. In this method or approach it examines the buying habits of the customers by identifying the associations among the items purchased by the customers in their baskets. This helps to increase in the sales of a particular product by identifying the frequent items purchased by the customers. This paper mainly focuses on the study of the existing data mining algorithm for Market Basket data.

A consensus exists that readability is an essential determining characteristic of code quality, but not about which factors contribute to human notions of software readability the most. We define readability as a human judgment of how easy a text is to understand. The readability of a program is related to its maintainability, and is thus a key factor in overall software quality. Typically, maintenance will consume over 70 percent of the total lifecycle cost of a software product. While software complexity metrics typically take into account the size of classes and methods and the extent of their interactions, the readability of code is based primarily on local, line-by-line factors. Our notion of readability arises directly from the judgments of actual human annotators who do not have context for the code they are judging. We present a descriptive model of software readability based on simple features that can be extracted automatically from programs. This model of software readabi...

Mining of the Data now days plays a major role and concern in the present world in the industry and also in the research areas. Many tools and application software’s are evolved to mine the data and to present the best data visualization. One of the Data Mining tool available is WEKA, in which contain many machine level and other types of algorithms. In the present paper the data classification is a medical dataset of diabetes category in which we cluster the dataset using various clustering algorithms like EM, k-means, OPTICS and the results are depicted. Keywords— Dataset, Data Mining, Clustering, Weka.