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Electronic commerce includes all business conduct through information and communication technology. Development of infrastructure, telecommunications, mobile technologies, the internet and social media in recent years, made a tremendous growth in business through e-commerce. Now e-commerce is a vital part of the economic development and helps in employment, FDI and GDP growth in the country. More...
Dynamic data if used properly can bring huge benefits to the humanity, science and business. The various properties of dynamic data like volume, velocity, variety, variation and veracity render the current methods of data analysis ineffective. Dynamic data analysis needs fusion of methods for the data mining with those of machine learning. The k-means algorithm is one such algorithm that has existed...
Mining frequent closed sequential pattern (FCSPs) has attracted a great deal of research attention, because it is an important task in sequences mining. In recently, many studies have focused on mining frequent closed sequential patterns because, such patterns have proved to be more efficient and compact than frequent sequential patterns. Information can be fully extracted from frequent closed sequential...
Cloud Computing has many significant benefits like the provision of computing resources and virtual networks on demand. However, there is the problem to assure the security of these networks against Distributed Denial-of-Service (DDoS) attack. Over the past few decades, the development of protection method based on data mining has attracted many researchers because of its effectiveness and practical...
Clustering analysis has the very broad applications on data analysis, such as data mining, machine learning, and information retrieval. In practice, most of clustering algorithms suffer from the effects of noises, different densities and shapes, cluster overlaps, etc. To solve the problems, in this paper, we propose a simple but effective density-based clustering framework (DCF) and implement a clustering...
Business Intelligence proves to be extremely useful to a vendor in order to raise the sales and product performance of products. It is an essential aspect to take business conclusions into account. There is massive data on social media that can be exploited to give us useful information. The present paper deals with a system created to exhibit intelligence. This system speculates the sales performance...
Opinions about the data has been an important part of analyzing the opinions and sentiments. The sentiment analysis is a major part of data mining that has important applications in various fields. The novice customers get into any field by only getting reviews from the various websites or reviewers. The reviews are not necessarily correct all the time. So, we need to first analyze them and then put...
Nowadays, the analysis of social networks, as well as the community evolution has become a hotly discussed topic in social computing field. In this paper, we focus on mining and tracking the dynamic communities based on social networking analysis. Based on a generic framework for the dynamic community discovery, a computational approach is developed to extract users' static and dynamic features for...
The creation of Graphical User Interfaces (GUI) for the Electronic Health Record (EHR) based on archetypes improves maintainability of Health applications and reduces dependence on a software team. However, we noticed there lacked approaches to build customizable GUIs for the EHR, and performed experimental tests to assess the usability of Health applications created from archetypes. This paper presents...
High utility itemset mining provides more useful and realistic results than frequent pattern mining because of its ability to consider statistical correlation and semantic significance among the items. The state of art algorithms designed for mining high utility itemsets always consider the database as static. If they are used for dynamic databases for the same purpose, database is rescanned from...
Data sets are the backbone for data mining and knowledge engineering field. The class imbalance problem exists in many real-time data sets. In this paper we investigate the existing approaches for class imbalance problem in the context of classification and ordinal classification. In particular, this investigation extends the study of issues in ordinal classification with respect to the data set and...
Data Mining in non-stationary data streams is gaining more attentionrecently, especially in the context of Internet of Things and Big Data. It is a highly challenging task, since the fundamentally different typesof possibly occurring drift undermine classical assumptions such asi.i.d. data or stationary distributions. Available algorithms are either struggling with certain forms of drift or require...
We develop and analyze an unsupervised and domain-independent method for extracting keywords from single documents. Our approach differs from the previous ones in the way of identifying candidate keywords, pruning the list of candidate keywords with several filtering heuristics and selecting keywords from the list of candidate keywords according to dynamic threshold functions. We were able to obtain...
In this paper, we present a dynamic clustering algorithm that efficiently deals with data streams and achieves several important properties which are not generally found together in the same algorithm. The dynamic clustering algorithm operates online in two different time-scale stages, a fast distance-based stage that generates micro-clusters and a density-based stage that groups the micro-clusters...
It is widely observed that the poor event logs quality poses a significant challenge to the process mining project both in terms of choice of process mining algorithms and in terms of the quality of the discovered process model. Therefore, it is important to control the quality of event logs prior to conducting a process mining analysis. In this paper, we propose a qualitative model which aims to...
In this paper, a new metaheuristic algorithm is developed, suitable for solving combinatorial optimization problems, such as the job shop scheduling problems, the travelling salesman problem, the vehicle routing problem, etc. This study focuses on permutation flow-shop scheduling problem. The proposed algorithm combines various techniques used in local search. As various elements of the proposed algorithm...
While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time series mean that the combination of nearest neighbor classifiers and Dynamic Time Warping (DTW) is very competitive across a host of domains, from medicine to astronomy to environmental sensors. While there has been significant progress in improving the...
Recent years have witnessed the prevalence of networked data in various domains. Among them, a large number of networks are not only topologically structured but also have a rich set of features on nodes. These node features are usually of high dimensionality with noisy, irrelevant and redundant information, which may impede the performance of other learning tasks. Feature selection is useful to alleviate...
Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively...
The analysis of the temporal evolution of dynamic networks is a key challenge for understanding complex processes hidden in graph structured data. Graph evolution rules capture such processes on the level of small subgraphs by describing frequently occurring structural changes within a network. Existing rule discovery methods make restrictive assumptions on the change processes present in networks...
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