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Editorial Message: Special Issue on Fuzzy System in Data Mining and Knowledge Discovery: Modelling and Application

in Data mining and Knowledge discovery: Modelling and application, we would like to publish the latest, innovative, and outstanding research results in the International Journal of Fuzzy Systems (IJFS ... ). Fuzzy system in data mining and knowledge discovery is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. Data mining and knowledge discovery has studied

The convergence computing model for big sensor data mining and knowledge discovery

model of cloud computing and cloud storage today is the basis for the accumulation of the results of data mining and knowledge discovery. Means of communication and remote access can solve the problem of ... . Convergence computing; Wireless sensor network; Fog computing; GRID; Cloud computing; Cloud storage; Mobile computing; Data mining; Knowledge discovery - Sensor data is one of the big data, and its

Recent granular computing frameworks for mining relational data

A lot of data currently being collected is stored in databases with a relational structure. The process of knowledge discovery from such data is a more challenging task compared with single table ... different data mining tasks. A theoretical unifying framework can provide a concise look at the field of data mining as well as contribute to improving the process of knowledge discovery. The problem of

A framework for simulation-based multi-objective optimization and knowledge discovery of machining process

by using FE simulations. The trade-off solutions obtained by MOO are presented. A knowledge discovery study is carried out on the trade-off solutions. The non-dominated solutions are analyzed using a ... innovization for discovering useful design principles: case studies from engineering . Appl Soft Comput 15 : 42 - 56 30. Bandaru S , Ng AHC , Deb K ( 2017 ) Data mining methods for knowledge discovery in multi

Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications

learning complex data refer to an advanced study area of data mining and knowledge discovery concerning the development and analysis of approaches for discovering patterns and learning models from data with ... social, economic, scientific, and engineering applications. Complex data pose new challenges for current research in data mining and knowledge discovery as they require new methods for processing, mining

Textual Data Mining For Knowledge Discovery and Data Classification: A Comparative Study

data. Data Mining techniques have long been applied mainly to numerical forms of data available from various data sources but their applications to analyse semi-structured or unstructured databases are ... task of knowledge discovery but the definition used in this research work is, “the process of discovering valuable information or knowledge from textual data through the application of data mining

Knowledge Graph Representation via Similarity-Based Embedding

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method ... increasingly more essential role in different domains [1]: question answer system [2, 3], information retrieval [4], semantic parsing [5], named entity disambiguation [6], biological data mining [7, 8], and so

Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery

, 215123 , China 2 Division of Computing Science and Maths, University of Stirling , Stirling FK9 4LA, Scotland , UK Knowledge discovery is an emerging topic in many domains addressing a variety of ... methodologies for extracting useful knowledge from data. In an era of explosive data growth, together with wide-spreading powerful distributive and parallel computing, we are faced with an urgent demand for

Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF

Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively ... be used for marketing the product. - Association rule mining, data mining, ECLAT, mining 1 Introduction Data mining is an important research domain is currently focused on knowledge discovery in

Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF

Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively ... be used for marketing the product. - Association rule mining, data mining, ECLAT, mining 1 Introduction Data mining is an important research domain is currently focused on knowledge discovery in

Mining shopping data with passive tags via velocity analysis

Unlikeonline shopping, it is difficult for the physical store to collect customer shopping data during the process of shopping and conduct in-depth data mining. The existing methods to solve this ... passive tag-based shopping data mining method, and our experiment results demonstrate the feasibility and accuracy of the proposed approach. To the best of our knowledge, our work is one of first to deal

Lawsuit lead time prediction: Comparison of data mining techniques based on categorical response variable

keywords length of judicial proceedings, court delay, disposition time, filings court time and time to court case resolution were searched to identify the variable lead time. The keywords data mining

Mining and visualising contradictory data

Big datasets are often stored in flat files and can contain contradictory data. Contradictory data undermines the soundness of the information from a noisy dataset. Traditional tools such as pie ... herein explain how contradictory data can be mined and visually analysed using ConTra. ConTra is an application developed by the authors of this work. It applies the mutual exclusion rule in mining

Algorithms for frequent itemset mining: a literature review

shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining ... international conference on advances in social networks analysis and mining (ASONAM) , San Francisco Gullo F ( 2015 ) From patterns in data to knowledge discovery: what data mining can do . Phys Proc 62 : 18 - 22

Application of Kansei engineering and data mining in the Thai ceramic manufacturing

needs is the target of designers and to develop a product that must satisfy customers. This research is applied Kansei Engineering (KE) and Data Mining (DM) into the customer driven product design process ... one of the ‘‘Knowledge Discovery in Databases’’ processes. DM can be used to explore numerus volumes of data to discover the hidden patterns and the interactions of highly complex dataset (Zayed et al

Use of data mining techniques to classify soil CO2 emission induced by crop management in sugarcane field

better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data ... soil. Among the techniques of data mining, classification is a task that stands out in the studies of the scientific community of Knowledge Discovery in Database (KDD). The principle of this task is to

A data mining framework for environmental and geo-spatial data analysis

sequence of data mining process including clustering algorithm, analysis technique and pattern changing discovery algorithm. In contrast to previous works in this area, our approaches can cluster and analyze ... data constitute enriched geo-spatial data. Data mining techniques are needed to facilitate the information extraction and knowledge discovery from such enriched geo-spatial data. In particular, we

A state-of-the-art survey of malware detection approaches using data mining techniques

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed ... primary classifications that include dynamic and static methods [ 11, 12 ]. To the best of our knowledge, the most data mining methods have some benefits and weaknesses in malware detection subject [ 13

The NASA Astrophysics Data System: Architecture

. Bibliographic records are stored as a corpus of structured documents containing fielded data and metadata, while discipline-specific knowledge is segregated in a set of files independent of the bibliographic data ... searching engines and \Data pull" is the activity of retrieving data from one or incorporate discipline-speci c knowledge in a set of con gmore remote network locations. According to this model, uration and

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families

sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data ... automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct