AutoML classifier clustering procedure

Abstract

Recommendation systems are one of the main applications of machine learning (ML) used across different industries. This paper presents a new automated machine learning (AutoML) method of providing recommendations by processing data sets using ML algorithms, targeting, and offering cluster recommendations for new observations and as a new decision support method. The AutoML conducts a complete procedure and includes analysis and division of data into an efficient number of clusters. We apply the k‐means, using the elbow method to calculate costs per cluster, followed by analyzing the allocation of the data into the clusters, thus providing a method for prediction and for allocating new observations to the relevant clusters (knn). This study includes two experiments using the complete AutoML procedure conducted on a data set, with more than two million records and dozens of attributes. This was done to demonstrate how the AutoML method can be implemented and successfully run with a high‐capacity analysis procedure. The motivation was to analyze, examine, assign, and integrate new observations into existing clusters that have been defined. The results showed that the AutoML method provided efficient recommendations for new observations with an accuracy rate of 99.99%. Hence, the AutoML procedure can offer a full system for any organization to efficiently split existing data into clusters, assign to clusters, and predict the cluster allocation of new observations. The significant contribution of this study is a simple method that can achieve fast and high accuracy clustering for ongoing (new) classified data acquired by an organization.

DLLog: An Online Log Parsing Approach for Large-Scale System

“…For a given current log word frequency sequence S, the frst step is to search for log templates in the existing log template set Tem total with the same log type and component name as the current log word frequency sequence S. Tese matched log templates form a new set, Tem same . Ten, we calculate the matching degree between each log template in Tem same and the current log word frequency sequence S using the longest common subsequence (LCS) method [32][33][34]. Te matching degree is determined by the length of the longest common subsequence.…”

Audio Style Conversion Based on AutoML and Big Data Analysis

“…Audio style conversion, as an important branch in the field of audio processing, has always been the focus of researchers. The transformation of audio style aims to make subtle adjustments to the characteristics of audio without loss, such as time domain, frequency domain, timbre, pitch, etc., while retaining the essential information of audio [1][2]. The implementation of this transformation has a profound impact on many fields such as music production, speech synthesis, and oral teaching [3][4].…”

Weighted distance classification method based on data intelligence

“…Many learning algorithms are based on distance metrics, which input two vectors of the same dimension and return a value representing their distance. The smaller the value, the closer or more similar the vectors are to each other (O. Koren et al, 2022). In the weighted distance metric, features are assigned different weights based on their distance (Fu et al, 2016; Yujian & Bo, 2007).…”

“…The smaller the value, the closer or more similar the vectors are to each other (O. Koren et al, 2022). In the weighted distance metric, features are assigned different weights based on their distance (Fu et al, 2016;Yujian & Bo, 2007).…”