Wyniki wyszukiwania - BazTech

1

A Novel Cluster Ensemble Based on a Single Clustering Algorithm

Khan Tahseen, Tian Wenhong, Kadhim Mustafa R., Buyya Rajkumar

Annals of Computer Science and Information Systems

|

2021

|

Vol. 25

127--135

EN

In recent years, several cluster ensemble methods have been developed, but they still have some limitations. They often use different clustering algorithms in both stages of the clustering ensemble method, such as the ensemble generation step and the consensus function, resulting in a compatibility issues. To deal with it, we propose a novel cluster ensemble method based on an identical clustering algorithm (CEI). Experiments on real-world datasets from various sources show that CEI improves accuracy by 5% on average compared to state-of-the-art cluster ensemble methods and by 55.54% compared to AP while consuming 44.60% less execution time.

2

Dynamic Clustering Personalization for Recommending Long Tail Items

Vinícius de Sousa Silva Diogo, Araújo Durão Frederico

Annals of Computer Science and Information Systems

|

2020

|

Vol. 21

417--425

EN

Recommendation strategies are used in several contexts in order to bring potential users closer to products with a strong probability of interest. When recomendations focus on niche items, they are called recommendations in the long tail. In these cases, they also look for less popular items and try to find your target custumer, niche market. This paper proposes a long tail recommendation approach that prioritizes relevance, diversity and popularity of recommended items. For that, a hybrid approach based on two techniques are used. The first is clustering with dynamic parameters that adapt from according to the dataset used and the second is a type of Markov chains for to calculate the distance of interest of a user to an item of relevance for this user. The results show that the techniques used have a better relevance indexes at the same time more diverse and less popular recommendations.

3

Data Mining for Process Modeling: A Clustered Process Discovery Approach

Cirne Renato, Melquiades Caio, Leite Renan, Leijden Eronita, Maciel Alexandre, Buarque de Lima Neto Fernando

Annals of Computer Science and Information Systems

|

2020

|

Vol. 21

587--590

EN

Process mining has emerged as a new scientific research topic on the interface between process modeling and event data gathering. In the search for process models that best fit to reality, the process discovery approach of creating referential processes from observed behavior. However, despite these methods showing relevant results, when faced with noisy and divergent tendencies they end up producing limited results. This work proposes the application of process discovery technique, combined to cluster technique k-means, to generate new process models, considering its conformance checking measures. The proposed solution is applied to an ad hoc workflow. And as a result, the use of the clustering techniques coupled with process discovery showed significant gains in the generation of process models, unlike the standard approach.

4

Handling of categorical data in software development effort estimation: a systematic mapping study

Amazal Fatima Azzahra, Idri Ali

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

763--770

EN

Producing reliable and accurate estimates of software effort remains a difficult task in software project management, especially at the early stages of the software life cycle where the information available is more categorical than numerical. In this paper, we conducted a systematic mapping study of papers dealing with categorical data in software development effort estimation. In total, 27 papers were identified from 1997 to January 2019. The selected studies were analyzed and classified according to eight criteria: publication channels, year of publication, research approach, contribution type, SDEE technique, Technique used to handle categorical data, types of categorical data and datasets used. The results showed that most of the selected papers investigate the use of both nominal and ordinal data. Furthermore, Euclidean distance, fuzzy logic, and fuzzy clustering techniques were the most used techniques to handle categorical data using analogy. Using regression, most papers employed ANOVA and combination of categories.

5

An approach to customer community discovery

Korczak Jerzy, Pondel Maciej, Sroka Wiktor

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

675--683

EN

In the paper, a new multi-level hybrid method of community detection combining a density-based clustering with a label propagation method is proposed. Many algorithms have been applied to preprocess, visualize, cluster, and interpret the data describing customer behavior, among others DBSCAN, RFM, k-NN, UMAP, LPA. In the paper, two key algorithms have been detailed: DBSCAN and LPA. DBSCAN is a density-based clustering algorithm. However, managers usually find the clustering results too difficult to interpret and apply. To enhance the business value of clustering and create customer communities, the label propagation algorithm (LPA) has been proposed due to its quality and low computational complexity. The approach is validated on real life marketing database using advanced analytics platform Upsaily.

6

Environment adaptive lighting systems for smart homes

Catalbas M. C.

Advances in Science and Technology. Research Journal

|

2017

|

Vol. 11, no 3

172--178

EN

In this work, an application of adaptive lighting system is proposed for smart homes. In this paper, it is suggested that, an intelligent lighting system with outdoor adaptation can be realized via a real fisheye image. During the implementation of the proposed method, the fuzzy c-means method, which is a commonly used data clustering method, has been used. The input image is divided into three different regions according to its brightness levels. Then, the RGB image is converted to CIE 1931 XYZ color space; and the obtained XYZ values are converted to x and y values. The parameters of x and y values are shown in CIE Chromaticity Diagram for different regions in the sky. Thereafter, the coordinate values are converted to Correlated Color Temperature by using two different formulas. Additionally, the conversion results are examined with respect to actual and estimated CCT values.