Application of classification technique of data mining for employee management system

This paper presents the application of classification technique of data mining used for the Employee Management System (EMS). This paper discusses the classification techniques of data mining and based on the data, the process of Knowledge Discovery in Databases (KDD) is reformed for classifying large data into different categories such as Disability, Employee Performance, etc. This paper discusses, WEKA data mining toolkit classifier model to predict employee’s performance based on the employee’s age, date of joining and number of years of experience. This study helps to predict the employee’s work-cycle and helps the management to find the employee’s performance those who are disabled and enabled. The paper addresses the system to get the details of those employees who need special attention and guide the management to make policies to improve employees’ performance. We demonstrate the application in a real-life situation. © Springer International Publishing AG, part of Springer Nature 2018

Analysis of behavior of automatic learning algorithms to identify criminal messages

In this type of explanation, strictly economic or criminal motives predominate: mainly the control of routes and places, and the punishment of desertion or treason. The precarious and fragmentary nature of the public discourse of drug traffickers as well as the preponderance of police narratives has concealed the strictly political dimension of "criminal" violence in Colombia. In pragmatic terms, organized crime and politics are more similar than we would like to assume. They have in common the objective of dominating territories, resources and populations; both tend to stand as a system of "parasitic intermediation". Both mafias and the state offer "protection" in exchange for payment of fees, reward loyalty and punish treason. It is the discursive acts that accompany violence and the series of institutional procedures in which they are registered that allow us to draw the line between the political and the criminal, the legitimate and the illegitimate, the just and the unjust. In Colombia, that border has lost clarity. In this study, an analysis of narco-messages found in banners, social networks and other databases is carried out by applying data mining, in order to propose a geospatial model through which it is possible to identify and geographically distribute the authors of the messages

Business analytics

El ingeniero Industrial debe tener la capacidad de extraer conocimiento a partir de diferentes medios de almacenamiento de datos, lo cual permite visualizar la importancia del la minería de datos para extraer patrones e información de las grandes cantidades de datos que se están generando en los procesos, productivos, de gestión de clientes e interacción con plataformas web, entre otros. Las técnicas de minería de datos supervisadas y no supervisadas le permitirán a los estudiantes tratar distintos conjuntos de datos acerca de temáticas especificas lo que conduce a la adquisición de nuevo conocimiento que servirá para la toma de decisiones asertiva en los diferentes medios, como lanzar una campaña comercial, o tomar acciones en relación con el mantenimiento de equipos, entre otros

Innovación de la gestión del talento humano

El programa de esta asignatura, fortalece la innovación de la gestión del talento humano y competitividad humana, representando originales formas de organización del talento humano partiendo de la gestión por competencias y la organización de las compensaciones A partir del desarrollo de la asignatura se logrará lo planeado con prácticas innovadoras, para ello, se alinean los propósitos tanto del colaborador como el de la organización, consolidando integralmente las metas empresariales de intereses en común concertados, mediado por los subsistemas de: provisión, organización, mantenimiento, desarrollo y auditoria. Ubicando estratégicamente al talento humano en puestos medulares de acuerdo a sus competencias, involucrando activamente a su personal en la cadena socio productivo, generado valor agregado, garantizando la optimización sistémica de los recursos: humanos, físicos y económicos. Todo esto impacta la fidelización de sus clientes, capacidad relacional con sus redes corporativas, stakeholder, clúster, y atracción de nuevos talentos que buscan la gestión de conocimiento

Design and development of a custom system of technology surveillance and competitive intelligence in SMEs

Making strategic decisions is a complex process that requires reliable and up-to-date information. It is therefore necessary to have tools that facilitate the information management. Technology Surveillance (TS) and Competitive Intelligence (CI) are two disciplines that seek to obtain accurate and up-to-date information. Clearly, the web is the largest and most important source of information, but their destructuring and disorganization requires tools that help to manage it. This work presents a model for TS and CI using Web Mining techniques such as ranking algorithm of web pages based on machine learning, i.e. the Advanced Cluster Vector Page Ranking (ACVPR) algorithm

Recommendation of collaborative filtering for a technological surveillance model using Multi-Dimension Tensor Factorization

Technological surveillance in research centers and universities focuses on carrying out a systematic follow-up on the development of research lines, the research leaders, the possibilities of scientific-technological collaboration, and to the knowledge of current trends from research. All these elements allow guiding the researches and supporting the scientific-technological strategy. This research proposes a model of technological surveillance supported by a recommendation system as an application that focuses on the preferences of researchers in universities and research centers. The multidimensional tensor factorization approach, based on grouping to build a recommendation system and to validate the increase in tensors, improves the accuracy of the recommendation. The experiments have been carried out in real data sets as the university and research centers. The results confirm that the dispersion issues are improved within a wider margin in both data sets. In addition, the proposed approach states that the increase in the number of dimensions shows a 7-10% improvement in accuracy and memory, which increases performance as an expert recommendation system

Association rules extraction for customer segmentation in the SMEs sector using the apriori algorithm

Data Mining applied to the field of commercialization allows, among other aspects, to discover patterns of behavior in clients, which companies can use to create marketing strategies addressed to their different types of clients. This research focused on a database, the CRISP-DM methodology was applied for the Data Mining process. The database used was that corresponding to the sector of SMEs and referring to customers and sales, the analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and on this model the grouping algorithms were applied: k -means, k-medoids, and SelfOrganizing Maps (SOM). To validate the result of the grouping algorithms and select the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers

Implementation of ID3 algorithm classification using webbased weka

The Bangil District Court is an IB class court that handles a large number of case cases. Every year more and more case cases are included in the Bangil District Court, but not all case cases are in a mutation status. By using classification techniques that can process large amounts of data to find patterns that occur in case data. Data processing is used to predict case minutation with the decision tree method using ID3 algorithm. Case data has 8 attributes and has been classified into 6 parts, namely division based on Case Type, Register, Case Classification, Length of Process, Public Prosecutor and Decision with a goal of Mutation Status. Weka 3.6 is an API that is used to build rules / rule bases. The rule that was formed was then implemented in the making of a case status prediction application in the web-based Bangil District Court

Improvements for determining the number of clusters in k-means for innovation databases in SMEs

The Automatic Clustering using Differential Evolution (ACDE) is one of the grouping methods capable of automatically determining the number of the cluster. However, ACDE continues making use of the strategy manual to determine the activation threshold of k, which affects its performance. In this study, the problem of ACDE is enhanced using the U Control Chart (UCC). The performance of the proposed method was tested using five data sets from the National Administrative Department of Statistics (DANE - Departamento Administrativo Nacional de Estadísticas) and the Ministry of Commerce, Industry, and Tourism of Colombia for the innovative capacity of Small and Medium-sized Enterprises (SMEs) and were assessed by the Davies Bouldin Index (DBI) and the Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering