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Exploiting mobile base stations in wireless sensor network increases longevity as it is very effective in dealing with the power hole problem. Mobile sinks are elements used to collect data from network sensors nodes. The movement of the sink eventually changes the sensors of a single hop which will prevent the formation of the hotspot thereby improving the life of the network to a critical level. In this paper we propose the Greedy Heuristic Protocol for Data Collection called GHPDC which selfishly selects energy-rich sites for a mobile channel. Apart from this the paper also proposes the formation of Linear Programming which enhances the station duration which will have a direct impact on prolonging the life of the network. The design of the system line takes into account the residual power of the sensor nodes to determine the amount of time for the base station to halt.

Data mining automates the process of finding predictive records in large databases. Clustering is a very popular technique in data mining and is a significant methodology that is performed based on the principle of similarity. The segregation of a large database is a challenging and time consuming task. For this purpose, an approach called data labeling through sampling technique is used. Using this approach segregating large databases not only gets easier but also it increases the efficiency of clustering technique. Initially a sample data is retrieved from a large database for clustering and the residual unsampled data points are compared with the clustered data from which the similar data points are clustered and the dissimilar one are considered as outliers based on various data labeling techniques. These data labeling techniques are easier to apply in the numerical domains, whereas in the categorical domains this is a complicated task as the distance among data points are incalculable. Further the proposed methodology gives a data labeling technique based on the changes in the similarities after including unlabeled data point into existing cluster for categorical data using cluster entropy in rough set theory. The experimental results show that the proposed algorithm is an efficient and high quality clustering algorithm compared to that of the previous ones.

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Clustering is an important technique in data mining. Clustering a large data set is difficult and time consuming. An approach called data labelling has been suggested for clustering large databases using sampling technique to improve efficiency of clustering. A sampled data is selected randomly for initial clustering and data points which are not sampled and unclustered are given cluster label or an outlier based on various data labelling techniques. Data labelling is an easy task in numerical domain because it is performed based on distance between a cluster and an unlabelled data point. However, in categorical domain since the distance is not defined properly between data points and data points with cluster, then data labelling is a difficult task for categorical data. This paper proposes a method for data labelling using entropy model in rough sets for categorical data. The concept of entropy, introduced by Shannon with particular reference to information theory is a powerful mechanism for the measurement of uncertainty information. In this method, data labelling is performed by integrating entropy with rough sets. This method is also applied to drift detection to establish if concept drift occurred or not when clustering categorical data. The cluster purity is also discussed using Rough Entropy for data labelling and for outlier detection. The experimental results show that the efficiency and clustering quality of this algorithm are better than the previous algorithms.

In present research, Data mining is become one of the growing area which deals with data. Clustering is recognized as an efficient methodology in data grouping; to improve the efficiency of the clustering many researchers have used data labeling method. Labeling method works on similar data points, into the proper clusters. In categorical domain applying data labeling is not so easy when compare with numerical domain. In numeral domain it is easy to find difference between to data points, but in categorical it is not easy. Since data labeling on categorical is a challenging issue till date and it is quite complex to implement. The proposed methodology is deals on this problem.  According proposed method a sample data will be taken. That sampled data further divides sliding windows, and then a normal clustering algorithm will be applied on one sliding window and divides into clusters. Rough membership Entropy function is used to find the similarity between unlabelled data points to labeled data points. The proposed methodology has two important features those are 1) The Data points will moved into their proper clusters, means the quality clusters will take places, 2) Proposed methodology will execute with high efficiency rate. In this paper the proposed methodology is applied on KDD Cup99 data sets, and the results shows appreciably more proficient than earlier works.

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