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Millions of users create user profiles on social media. Changes made to an attribute in the user profiles on social media generate a huge volume of data representing a data stream. A framework has been proposed to analyze such data streams and cluster the attribute values related to each other.
Micro array data play a vital role in simultaneously monitoring the expression profile of large number of genes that are specified with various experimental conditions. In bioinformatics research, the recognition of co-expressed and coherent patterns is a major objective in micro array data analysis. The K-means clustering algorithm is gaining popularity in the knowledge discovery domain for effectively...
In this digital world, we are facing the flood of data, but depriving for knowledge. The eminent need of mining is useful to extract the hidden pattern from the wide availability of vast amount of data. Clustering is one such useful mining tool to handle this unfavorable situation by carrying out crucial steps refers as cluster analysis. It is the process of a grouping of patterns into clusters based...
Data Clustering in Data Mining is a domain which never gets out of focus. Clustering a data was always an easy task but achieving the required accuracy, precision and performance was never so easy. K means being an archaic clustering algorithm got tested and experimented thousands of times with variety of datasets and other combination of algorithm due to its robustness and simplicity but what this...
In today's world, the communication and interaction among the people is through the social networks. Graphically these networks can be represented as the collection of nodes and edges. People belong to different communities depending upon their relations. The community detection in networks is the clustering of networks into communities in such a way that the connections between nodes within the same...
In this research, we consider the related problem of malware classification based on HMMs. We train HMMs for a variety of malware generators and a variety of compilers. The results of HMM are further classified using k means algorithm but k means algorithm has drawback of stuck into local minima so we optimized the k means with genetic algorithm (GA). Genetic algorithm (GA) tuned k means clustering...
Wireless sensor network is a set of independent transducers with communication infrastructure for recording and monitoring at different locations. The monitoring parameters are energy, temperature, humidity, pressure, direction and speed of the node in the WSN. The main challenges of WSN are efficiency, scalability, heterogeneity, reliability, robustness, privacy and security. Many researchers are...
A Wireless Sensor Network consists of enormous amount of sensor nodes. These sensor nodes sense the information from the sensing region and transmit to the sink. From the sink a client can access the required information. Essential part of the wireless sensor network is the sensor nodes that are driven with the batteries. Prolonging the network lifetime is very difficult and expensive in many conditions...
Clustering is one of the most useful methods for data gathering in distributed wireless sensor networks (WSNs). In additions, sensors in such environment are energy constrained and generate the huge amount of data due to redundant data transmission and thus reducing lifetime of networks. Moreover, there is need to use efficient clustering techniques for collecting relevant data from nodes to eliminate...
Timestamped data is any data that contains a timestamp. It could range from social networking posts, e.g. tweets, and traditional documents e.g., news articles, to sale price and volume of a traded stock in financial applications e.g., market indices. Although rich in content, the volume of these data is so high that it is challenging to get insight into the bulk at hand with minimal effort. In the...
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