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Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
There are a diverse set of products for a particular type on the internet. When any user tries to find out best product among a certain type it is very much difficult to do it manually go through every one of them. That's why manually searching is not very efficient. In that scenario, recommendation system plays a great important role to recommend the best products. In this study, we develop a recommendation...
How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs...
With advances in technology, high volumes of a wide variety of valuable data of different veracity can be easily collected or generated at a high velocity in the current era of big data. Embedded in these big data are implicit, previously unknown and potentially useful information. Hence, fast and scalable big data science and engineering solutions that mine and discover knowledge from these big data...
The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation...
The article focuses on the results of the research into scientific publications of the All-Russian Institute for Scientific and Technical Information of the Russian Academy of Sciences database (VINITI Database RAS) in different fields. The purpose of operation was to increase partition accuracy on the directions of large volumes of scientific data. This analysis was carried out on summaries of scientific...
Utilizing data for segmentation analysis can bring a streamlined way to get potential insight as of decision making support in a business organization. Using appropriate data analytical technique help the organizations in profiling their customer segments accurately. The result brings an effective marketing strategy. However, there are times in doing data analytic, the organization needs another variable...
The last years, huge masses of data are produced or extracted by computational systems and independent electronic devices. To exploit this resource, novel methods must be employed or the established ones may be altered in order to confront the issues that arise. One of the most fruitful techniques, in order to locate and use information from data sources is clustering, and k-means is a successful...
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...
The criminal behavior is a disorderliness that is a combined result of social and economic aspects. The crime rate has expanded and the activities of criminals have broaden in last few decades due to better communication system and transport. Crimes cause terror and damage our community enormously in several means. In cities and towns the crime trends rises due to fast developmental activities and...
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique. This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters. This approach is characterised...
The study of the dynamic behaviour of the solar radiation is a very important task for PV system efficiency. Hence, we propose in this paper, a time series data mining method to detect the underlying dynamic presents in hourly solar radiation time series. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster the solar radiation time series and detect noisy data. Moreover,...
K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering. Motivated by this, this paper proposes an optimized k-means clustering method along with three optimization principles named k*-means...
Association rule mining is one of the most relevant techniques in data mining, aiming to extract correlation among sets of items or products in transactional databases. The huge number of association rules extracted represents the main obstacle that a decision maker faces. Hence, many interestingness measures have been proposed to evaluate the association rules. However, the abundance of these measures...
Crime is one of the most predominant and alarming aspects in our society and its prevention is a vital task. Crime analysis is a systematic way of detecting and investigating patterns and trends in crime. In this work, we use various clustering approaches of data mining to analyse the crime data of Tamilnadu. The crime data is extracted from National Crime Records Bureau (NCRB) of India. It consists...
Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input...
Companies today are developing business strategies taking into consideration behavior of their customers through social networks, which have allowed to extract large amounts of relevant data about users. This is why it has been necessary to apply data mining techniques to find patterns that describe the preferences of users in different contexts. This paper describes the results of using data mining...
The ideological and political education of college students is facing enormous challenge under new situation. Thus, it is necessary to continuously adjust the way of ideological work and strengthen the exchanging of advanced experience, to improve the ideological and political education effects for colleges. Based on the clustering method of data mining, this paper studies its application on ideological...
In today's world, where we generate large amount of data, we can harness the benefits of the hidden information i.e. patterns or correlations in these data. This information can be used in various constructive fields only if we are able to handle big data efficiently. One such process that is used to extract and handle the hidden information is data mining. There are various techniques in data mining...
Data Clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters. Partitioning...
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