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This paper studies design and implementation of precision marketing system on business platform of telecom network package, and proposes the analysis and mining technology based on distributed processing technology, for massive business payment data. Then the mining results are applied to final scheme of precision marketing strategy implementation. The scheme uses K-Means to segment users-based business...
Radio environment maps are geographical maps with overlaid performance information of radio communication systems, that can be considered as one of the key enablers for self-organizing networks. After the introduction of Minimization of Drive Tests (MDT) to the standards, operators are interested to generate radio environment maps based on sparse sets of MDT measurements gathered from user equipment...
Nowadays, many organizations collect large volumes of event log data on a daily basis, and the analysis of collected data is a challenging task. For this purpose, data mining methods have been suggested in past research papers, and several data clustering algorithms have been developed formining line patterns from event logs. In this paper, we introduce an open-source tool called LogClusterC which...
Today's large-scale supercomputers are producing a huge amount of log data. Exploring various potential correlations of fatal events is crucial for understanding their causality and improving the working efficiency for system administrators. To this end, we developed a toolkit, named LogAider, that can reveal three types of potential correlations: across-field, spatial, and temporal. Across-field...
As a popular topic model, Probabilistic Latent Semantic Analysis (PLSA) has been widely applied in text clustering due to its reliability and practicability. While independence assumption contributes to its practicability, it loses the rich local information between words, which in some cases will result in incoherent topics. In this paper, we propose an enhanced PLSA model embedded with word correlation...
Statistical analysis is widely used for problem solving in different fields. We present a research on Saint Petersburg morbidity rate. The aim of the work is to detect the heterogeneity in districts of the city with respect to morbidity rate, which was chosen as an indicator of population health. Methods of cluster analysis was utilized for grouping districts to homogeneous sets. Clustering can be...
In commercial banks, data centers often integrates different data sources, which represent complex and independent business systems. Due to the inherent data variability and measurement or execution errors, there may exist some abnormal customer records (data). Existing automatic abnormal customer detection methods are outlier detection which focuses on the differences between customers, and it ignores...
Datasets obtained through recently advanced measurement techniques tend to possess a large number of dimensions. This leads to explosively increasing computation costs for analyzing such datasets, thus making formulation and verification of scientific hypotheses very difficult. Therefore, an efficient approach to identifying feature subspaces of target datasets, that is, the subspaces of dimension...
With progress in the area of computer science, it is achievable to read, process, store and generate information out of the available data. Humongous amount of data is generated, which is of mixed type, including time-series, Boolean, spatial-temporal and alpha-numeric data. This data is generated at a very giant speed and volume, which makes difficult for the traditional clustering algorithms to...
Buildings and adjacent objects in the high spatial resolution images Present the spatial correlation due to the spectral similarity. In addition, the spectral details of building top are completely reflected in images because of resolution levels increased from meter to sub-meter. K-means algorithm is a classical clustering algorithm. Fine spectrum, low signal to noise ratio (SNR) and high spatial...
Crimes are a social irritation and cost our society deeply in several ways. Any research that can help in solving crimes quickly will pay for itself. About 10% of the criminals commit about 50% of the crimes [9]. The system is trained by feeding previous years record of crimes taken from legitimate online portal of India listing various crimes such as murder, kidnapping and abduction, dacoits, robbery,...
Recently, the multi-label learning has drawn considerable attention as it has many applications in text classification, image annotation and query/keyword suggestions etc. In recent years, a number of remedies have been proposed to address this challenging task. However, they are either tree based methods which has the expensive train costs or embedding based methods which has relatively lower accuracy...
The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity...
Live virtual machine (VM) migration is defined as a technique that migrates the entire operating system (OS) and its associated applications from one host/physical server to another providing that users should not notice any interruption in their services. Live VM migration plays an important role to facilitate online maintenance, load balancing, and energy management as part of resource management...
With more companies turning towards cloud computing for storage and processing of their data, the security of the cloud becomes essential. However, cloud computing is vulnerable to many security threats, including data leakages, compromised credentials, presence of unauthorized users or entities, execution of insecure applications or programming interfaces and APIs, shared technology vulnerabilities,...
In this paper a novel super-resolution wavefront extraction algorithm is introduced that merges advantages of the efficient constant false alarm rate and super-resolution ability of dynamic correlation method based techniques. The introduced algorithm optimizes the computational effort and possible feature extraction of complex metallic objects. Furthermore, a suitable imaging algorithm is briefly...
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
We introduce a new algorithm that maps multiple instance data using both positive and negative target concepts into a data representation suitable for standard classification. Multiple instance data are characterized by bags which are in turn characterized by a variable number of feature vectors or instances. Each bag has a known positive or negative label, but the labels of any given instances within...
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for...
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