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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

Advancing climate science with knowledge-discovery through data mining

exponential growth of climate data combined with Knowledge-Discovery through Data-mining (KDD) promises an unparalleled level of understanding of how the climate system responds to anthropogenic forcing. To ... banking, advertising or biology have yet to be duplicated in climate science. In the last decade, however, several groups attempted to apply the so-called Knowledge-Discovery through Data mining (KDD)1,2 to

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

Text and Image Compression based on Data Mining Perspective

compression research focuses on the Compression perspective of Data Mining as suggested by Naren Ramakrishnan et al. wherein efficient versions of seminal algorithms of Text/Image compression are developed ... : The Complete Reference. Second edn.  Han, J, Cheng, H, Xin, D and Yan, X. 2007. Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery, 15(1): 55–86. DOI

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

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

CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

mining interest preferences of users in a particular field in the context of today’s big data. We mainly use a large number of user text data from microblog to study. LDA is an effective method of text ... both home-town and out-of-town users,” in Proceeding of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1143, 2017. D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, and

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

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

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

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

A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation

and knowledge granulation theories is proposed to ascertain the characteristics weight. Experimental results on several UCI data sets demonstrate that the weighting method can effectively avoid ... redundant characteristics. 1. Introduction In data mining, in order to effectively classify the knowledge, we need to make proper assessment on the knowledge characteristics sets. Therefore, it is very

Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from “university” to “business” and “analytics”

I survey the transformation of the data mining and knowledge discovery field over the last 10 years from the unique vantage point of KDnuggets as a leading chronicler of the field. Analysis of the ... years the data mining and knowledge discovery field went through enormous transformation, influenced by seismic external forces such as the enormous growth of web/e-commerce, tremendous progress in

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

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