Evaluation of Chemical Data by Clustering Techniques

Obtaining more useful information by applying mathematical techniques from chemical data obtained by different methods can be defined as chemometry. The development of computer-equipped devices allows for obtaining a large number of data in the field of chemistry. Statistical methods and data mining principles are needed for the processing and evaluation of these data. Chemometry is briefly the investigation of how to perform meaningful calculations on data fly the investigation of how to perform meaningful calculations on data. In most cases, these calculations are too complex to be performed manually, and many different techniques are used in these processes, such as classification, clustering, data summarization, learning classification rules, finding dependency networks, variability analysis, and abnormal detection. In data mining, classification, and curve fitting are defined as prediction methods, while methods such as clustering and association analysis are described as descriptive. Classification is the examination of the attributes of data and assigning this data to a predefined class. The important thing here is that the specialties of each class are determined in advance. Clustering is the grouping of data according to their proximity or distance to each other, and there are no pre-defined group boundaries here, but it can be optimized by giving the number of groups. In given context, this study was aimed to group the measurement data obtained as a result of analyses with samples taken from raw wastewater from wastewater treatment plants using the clustering method to determine which cluster the new data to be measured are in and to estimate the BOD5 value related to these data without experimental measurement.