# A Survey of Big Data Archives in Time-Domain Astronomy
## 4 Astronomical Data Mining  
Due to the rapid development in data volume from various sky surveys, data repositories have grown in size from gigabytes to terabytes and petabytes, as discussed briefly in Section 2. The term \"big data analysis\" refers to analyzing large amounts of data. Astronomy is now a data-intensive science and is expected to become even more data-intensive in the next decade. Clustering and classification problems have traditionally been the norm for astronomers. For astronomers who use observational (experimental) methods to gather data on celestial objects and then analyze that data to determine the objects' physical properties and the underlying physics that underlies those properties, this is especially true [45]. Data mining is a collection of techniques used to reduce, improve, and clean a large amount of data. These techniques include summarization, classification, regression, clustering, association, time series analysis, and outlier/anomaly identification. The most important source of astronomical data is the systematic observation of the sky over a wide range of wavelengths. Scientific data mining has ensured the effectiveness and completeness of these data, leading to new astronomical research. In addition, numerous simulations generate large amounts of data [14]. Distributed data mining (DDM) is becoming more common as astronomical data sets (from many large sky surveys) grow too large to be downloaded to a single location for analysis. The discovery of hidden knowledge in geographically distributed heterogeneous databases is made possible by DDM algorithms [45]. Knowledge discovery in databases (KDD) is the primary emphasis of the data mining overview. However, the concept of a database encompasses machine-readable astronomical information [46]. The KDD focuses on extracting knowledge (high-level information) from low-level data (usually stored in large databases). KDD involves many steps such as data preparation and cleaning, data selection, sampling, preprocessing and transformation, data mining to extract patterns and models, interpretation and evaluation of extracted information, and evaluation, rendering, or use of the final extracted knowledge. However, it is important to keep in mind that data mining is only one part of the KDD process [47]. Many terms are related to data mining, and we begin by introducing some of them:  
* **Data Collection:** All actions necessary to gather the desired data in digital form were included in data collection. As a part of the research process, data collection methods include acquiring fresh observations, querying existing databases, and completing data mergers (data fusion). An enormous cross-matching dataset can introduce confusing matches, discrepancies in the point spread function (object resolution) inside or between data sets, adequate processing time, and data transit needs. A few arcseconds of astrometric tolerance are typically utilized when each database item lacks exact identification [48]. If the researcher chooses a method of collecting data based on a legitimate premise, he or she must weigh the method's strengths and weaknesses when analyzing their results. In qualitative research, it is critical that participants be recruited in a transparent manner [49]. * **Processing of data:** Data preprocessing, such as sample cuts in database searches, may be required during the data collection process. It is essential to use caution when preprocessing data because the input data can significantly affect many data mining approaches. For a specific algorithm, preprocessing can be divided into two types: procedures that make it meaningful for reading and processes that alter the data in some manner [48]. Data preprocessing includes the preparation and transformation of data so that it may be used in the mining process. Data preprocessing attempts to minimize data size, identify the relationships between data, normalize data, remove outliers, and extract characteristics from the data.