In the current era of big data, huge volumes a wide variety of valuable data are generated and collected at a high velocity. Hence, data science solutions are in demand to data mine these big data for valuable information and useful knowledge embedded ...

Due to popularity of social networking in the current era of big data, many social networking sites (e.g., Facebook) has been generating huge volumes of social data. In this paper, we aim to discover interesting relationships in a undirected (social) ...

This paper aims to explore the problems associated in solving the classification of cancer in gene expression data using deep learning model. Our proposed solution for the cancer classification of ribonucleic acid sequencing (RNA-seq) extracted from the ...

High volumes of valuable data and information can be easily collected in the current era of big data. As rich and constant sources of big data, an incredible amount of people from different social stratum take part in social networks. Hence, social ...

Pattern mining aims to discover from data the implicit, previously unknown and potentially useful information and knowledge in the form of patterns. Over the past 20 years, numerous pattern mining algorithms have been proposed. They focused on ...

Pattern mining has drawn attention of researchers because of its high applicability to mine patterns or sequences from probabilistic databases in various real-life applications. Weight of an item, a pattern, or a sequence help data scientists extract ...

For many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data mining---...

Advances in technology in the current era of big data has led to the high-velocity generation of high volumes of a wide variety of valuable data of different veracity. As rich sources of big data, social networks consist of users (or social entities) ...

In recent years, social media has played a huge role in how we share and communicate our thoughts and opinions. This information can very valuable for companies and governments as it can be used to analyze public mood and opinion which is a very ...

We describe a new method of voting system in social networks environment¹. We suggest a sequence of continuous support via a social network after electing representatives or exemplars in the network that is different from the typical majority voting. In ...

As one of data mining tasks, sequential pattern mining provides valuable information about frequent patterns of users over time. For instance, frequent sequential patterns can be applicable to analyze user clickstreams for determination of web ...

Data mining and analytics aims to analyze valuable data and extract implicit, previously unknown, and potentially useful information from the data. Due to advances in technology, high volumes of valuable data are generated at a high velocity in high ...

Frequent itemset mining discovers implicit, previously unknown and potentially useful knowledge---in the form of frequent itemsets---from data. For example, discovery of frequently purchased merchandise products reveals customer purchase patterns, which ...

Many social networking sites such as Facebook and Twitter have been used for sharing knowledge and information among social entities. Social entities in these social networks are often linked by some interdependency such as friendship or "following" ...

In the current era of Big data, high volumes of high-value data---such as social network data---can be generated at a high velocity. The quality and accuracy of these data depend on their veracity: uncertainty of the data. A collection of these ...

Data mining and machine learning approaches can be incorporated into business intelligence (BI) systems to help users for decision support in many real-life applications. Here, in this paper, we propose a machine learning approach for BI applications. ...

With advances in technology, streams of data are produced in many applications. Efficient techniques for extracting implicit, previously unknown, and potentially useful information (e.g., in the form frequent sets) from data streams are in demand. Many ...

As frequent pattern mining plays an important role in various real-life applications, it has been the subject of numerous studies. Most of the studies mine transactional datasets of precise data. However, there are situations in which data are ...

Frequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions ...

Most of the frequent pattern mining algorithms search for all frequent patterns. However, there are many real-life situations in which users are interested in only some tiny portions of the mined frequent patterns. For mining of constrained frequent ...

Huge volumes of streaming data have been generated by sensors for applications such as environment surveillance. Partially due to the inherited limitation of sensors, these continuous streaming data can be uncertain. Over the past few years, algorithms ...

Numerous frequent itemset mining algorithms have been proposed over the past two decades. Most of them mine traditional databases of precise data. However, there are many real-life applications for which data are uncertain. This leads to the mining of ...

With advances in technology, large amounts of streaming data can be generated continuously by sensors in applications like environment surveillance. Due to the inherited limitation of sensors, these continuous data can be uncertain. This calls for ...

Using information and communication technology (ICT) is a promising solution to assist and alleviate some of the existing problems in rural healthcare such as distance, lower number of care providers, communication, information integration, etc. In this ...

Frequent set mining searches for sets of items that are frequently co-occurring together. Existing algorithms mainly find all the frequent sets from precise data. However, there are real-life situations in which users are interested in only some tiny ...

Numerous algorithms have been proposed since the introduction of the research problem of frequent pattern mining. Such a research problem has played an essential role in many knowledge discovery and data mining (KDD) tasks. Most of the proposed frequent ...

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject ...

As frequent pattern mining plays an essential role in many knowledge discovery and data mining (KDD) tasks, numerous algorithms for finding frequent patterns have been proposed over the past 15 years. However, most of these algorithms return the mining ...

Many existing algorithms mine transaction databases of precise data for frequent itemsets. However, there are situations in which the user is interested in only some tiny portions of all the frequent itemsets, and there are also situations in which data ...

With advances in technology, mobile handheld devices-such as PDAs-have become very popular. In many real-life situations, users want to find structuring information using these mobile devices, which are convenient to use but have relatively limited ...

Data mining aims to search for implicit, previously unknown, and potentially useful pieces of information---such as sets of items that are frequently co-occurring together---that are embedded in data. The mined frequent sets can be used in the discovery ...

Mining of frequent patterns is one of the popular knowledge discovery and data mining (KDD) tasks. It also plays an essential role in the mining of many other patterns such as correlation, sequences, and association rules. Hence, it has been the subject ...

Over the past 15 years, numerous algorithms have been proposed for frequent pattern mining as it plays an essential role in many knowledge discovery and data mining (KDD) tasks. Most of these frequent pattern mining algorithms return the mined results ...

As technology advances, streams of data can be rapidly generated in many real-life applications. This calls for stream mining, which searches for implicit, previously unknown, and potentially useful information---such as frequent patterns---that might ...

In this paper, we apply computer science and software engineering techniques in, and bring benefits to, real-life applications. Specifically, we propose a design for a computeraided change detection system, which uses both image processing and data ...

Weather plays an important role in many areas such as agriculture. Having a clean set of agro-meteorological data removes doubt on weather-derived models and ensures confidence on decisions supported by these models. In this paper, we present our design ...

Data mining is supposed to be an iterative and exploratory process. In this context, we are working on a project with the overall objective of developing a practical computing environment for the human-centered exploratory mining of frequent sets. One ...

Since its introduction, frequent-set mining has been generalized to many forms, which include constrained data mining. The use of constraints permits user focus and guidance, enables user exploration and control, and leads to effective pruning of the ...