The tutorial will start by reviewing the similarities and differences between statistics, machine learning and data mining. Then we will take a closer look at the knowledge discovery process as described by the CRISP-DM methodology. Here we will focus on various types of machine learning algorithms used for the modeling step and on the statistical approaches and methods used in these algorithms...

In a recent paper, Jiménez et al. (2007) propose a ‘general’ and ‘interactive’ method for solving linear programming problems with fuzzy parameters. In this study, we propose a revision to the optimal crisp value of the objective function to eliminate the restrictive constraints imposed by Jiménez et al. (2007). The revised approach can be generalised to solve many real-world linear programming...

Internet banking has become widely available in Jamaica and yet there have been few studies to understand the characteristics of its users. For banks to improve their service and similar services it becomes imperative that they can justify the costs associated with these services. One of the ways these costs can be justified is if their customer base of internet banking users was to increase, t...

This paper presents a case study of data mining modeling techniques for direct marketing. It focuses to three stages of the CRISP-DM process for data mining projects: data preparation, modeling, and evaluation. We address some gaps in previous studies, namely: selection of model hyper-parameters and controlling the problem of under-fitting and over-fitting; dealing with randomness and 'lucky' s...

This paper presents the KEOPS data mining methodology centered on domain knowledge integration. KEOPS is a CRISP-DM compliant methodology which integrates a knowledge base and an ontology. In this paper, we focus first on the pre-processing steps of business understanding and data understanding in order to build an ontology driven information system (ODIS). Then we show how the knowledge base i...

Classification is a widely used technique in data mining. Thereby achieving a reasonable classifier performance is an increasingly important goal. This paper aims to empirically show how classifier performance can be improved by knowledge-driven data preparation using business, data and methodological know-how. To point out the variety of knowledge-driven approaches, we firstly introduce an adv...

BACKGROUND In Australia, screening for colorectal cancer (CRC) with colonoscopy is meant to be reserved for people at increased risk, however, currently there is a mismatch between individuals' risk of CRC and the type of CRC screening they receive. This paper describes the development and optimisation of a Colorectal cancer RISk Prediction tool ('CRISP') for use in primary care. The aim of the...

Industry 4.0 and its technologies allow advancements in communications, production management efficiency across several segments. In smart grids, essential parts of cities, meters act as IoT devices that can gather data help the sustainable energy matrix, a challenge is faced worldwide. This work aims to use meter household features seek most appropriate methods consumption prediction. Using Cr...

Despite the importance of small and medium-sized enterprises (SMEs) for growth development companies, high failure rate these companies persists, this correspondingly demands attention managers. Thus, to boost company success rate, we may deploy certain approaches, example predictive models, specifically SME innovation. This study aims examine variables that positively shape contribute towards ...

Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non-crisp (symbolic interval and fuzzy) numbers. ...