Data Mining In Large Databases Research Articles

Potential safety signals for antibacterial agents from the Brazilian national pharmacovigilance database (Vigimed/VigiFlow).

Antibacterial drugs are a widely used drug class due to the frequency of infectious diseases globally. Risks knowledge should ground these medicines’ selection. Data mining in large databases is essential to identify early safety signals and to support pharmacovigilance systems. We conducted a cross-sectional study to assess adverse drug events related to antibiotics reporting between December 2018 and December 2021 in the Brazilian database (Vigimed/VigiFlow). We used the Reporting Odds Ratio (ROR) disproportionality analysis method to identify disproportionate reporting signals (SDR), referring to statistical combinations between drugs and adverse events. Vancomycin was the most reported antibiotic (n = 1,733), followed by ceftriaxone (n = 1,277) and piperacillin and tazobactam (n = 1,024). We detected 294 safety signals related to antibacterials. We identified azithromycin leading in the number of safety signals (n = 49), followed by polymyxin B (n = 25). Of these, 95 were not provided for in the drug label and had little or no reports in the medical literature. Three serious events are associated with ceftazidime and avibactam, a new drug in the Brazilian market. We also found suicide attempts as a sign associated with amoxicillin/clavulanate. Gait disturbance, a worrying event, especially in the elderly, was associated with azithromycin. Our findings may help guide further pharmacoepidemiologic studies and monitoring safety signals in pharmacovigilance.

Application of Neural Network Overview In Data Mining

Data Mining is the term used to describe the process extract value / information from the database. Four things are needed in order to effectively data mining: data that has a high quality, right of data, examples of which are adequate, and the correct device. To obtain valuable information in the required data mining algorithms applied in data mining in large databases. There are a lot of complex algorithms in data mining. One is the so-called Neural networks have an important role in data mining.

Decision Tree Induction: An Approach for Data Classification Using AVL-Tree

Classification is considered to be one of the important building blocks in data mining problem. The major issues concerning data mining in large databases are efficiency and scalability. This paper addresses these issues by proposing a data classification method using AVL trees, which enhances the quality and stability. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining considered the issue of building a decision tree from the available data. Specifically, we consider a scenario in which we apply the multi level mining method on the data set and show how the proposed approach tend to give the efficient multiple level classifications of large amounts of data. The results specify the improved performance of the proposed algorithm which acquires designing rules from the knowledge database.

A search space reduction methodology for data mining in large databases

Given the present need for Customer Relationship and the increased growth of the size of databases, many new approaches to large database clustering and processing have been attempted. In this work, we propose a methodology based on the idea that statistically proven search space reduction is possible in practice. Two clustering models are generated: one corresponding to the full data set and another pertaining to the sampled data set. The resulting empirical distributions were mathematically tested to verify a tight non-linear significant approximation.

Multi-step density-based clustering

Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex distance measures are first choice but also simpler distance functions are available which can be computed much more efficiently. In this paper, we will demonstrate how the paradigm of multi-step query processing which relies on exact as well as on lower-bounding approximated distance functions can be integrated into the two density-based clustering algorithms DBSCAN and OPTICS resulting in a considerable efficiency boost. Our approach tries to confine itself to ɛ-range queries on the simple distance functions and carries out complex distance computations only at that stage of the clustering algorithm where they are compulsory to compute the correct clustering result. Furthermore, we will show how our approach can be used for approximated clustering allowing the user to find an individual trade-off between quality and efficiency. In order to assess the quality of the resulting clusterings, we introduce suitable quality measures which can be used generally for evaluating the quality of approximated partitioning and hierarchical clusterings. In a broad experimental evaluation based on real-world test data sets, we demonstrate that our approach accelerates the generation of exact density-based clusterings by more than one order of magnitude. Furthermore, we show that our approximated clustering approach results in high quality clusterings where the desired quality is scalable with respect to (w.r.t.) the overall number of exact distance computations.

ACODF: a novel data clustering approach for data mining in large databases

In this paper, we present an efficient clustering approach for large databases. Our simulation results indicate that the proposed novel clustering method (called ant colony optimization with different favor algorithm) performs better than the fast self-organizing map (SOM) combines K-means approach (FSOM+K-means) and genetic K-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+K-means approach and GKA.

ACODF: a novel data clustering approach for data mining in large databases

In this paper, we present an efficient clustering approach for large databases. Our simulation results indicate that the proposed novel clustering method (called ant colony optimization with different favor algorithm) performs better than the fast self-organizing map (SOM) combines K-means approach (FSOM+ K-means) and genetic K-means algorithm (GKA). In addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+ K-means approach and GKA.