Data analysis for clustering carpet manufacturing defects

Nowadays, with the latest technology, the accumulation of data has increased, and it has become more important to transform the stored raw data into information. The transformation of raw data into information increases earnings and can more easily be adapted to the competitive environment. Data mining is an approach based on statistical applications and machine learning algorithms in converting raw data into information. Data mining has been an important research area to find the hidden information/knowledge inside huge amounts of data. In this thesis, quality problems in carpet manufacturing are gathered in a database and the data are analyzed and clustering by using data mining algorithms. The main purpose is clustering carpet manufacturing defects by using different clustering algorithms WEKA software and find out which algorithm will be most suitable for the users. Also, this thesis is focusing on the improvement of data quality in databases with the help of current data cleaning methods. Thus, a deeper perspective on carpet quality problems is targeted and the number of customer complaints is expected to reduce in the long term. The data obtained from the database of a carpet producer were pre-processed and adapted to WEKA 3.9.4 software. Clustering algorithms which are used are partitioning based i.e K-means, Farthest First, Expectation maximization and Non Partitioning based i.e Cobweb. The best performing algorithm is Farthest First algorithm. It is taking less time then other clustering algorithm to find similar clusters through weka tool for quality problem in carpet manufacturing dataset.