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What are the Applications of Pattern Mining?
There are various applications of Pattern Mining which are as follows −
Pattern mining is generally used for noise filtering and data cleaning as preprocessing in several data-intensive applications. It can be used to explore microarray data, for example, which includes tens of thousands of dimensions (e.g., describing genes).
Pattern mining provides in the discovery of inherent mechanisms and clusters hidden in the data. Given the DBLP data set, for example, frequent pattern mining can simply discover interesting clusters like coauthor clusters (by determining authors who generally collaborate) and conference clusters (by determining the sharing of several authors and terms). Such architecture or cluster discovery can be used as preprocessing for additional sophisticated data mining.
Frequent patterns can be used effectively for subspace clustering in high-dimensional area. Clustering is difficult in high-dimensional space, where the distance among two objects is complex to measure. This is because such a distance is dominated by the multiple sets of dimensions in which the objects are occupying.
Pattern analysis is beneficial in the analysis of spatiotemporal information, timeseries data, image data, video data, and multimedia data. An application of spatiotemporal data analysis is the analysis of colocation patterns. These can help decide if a specific disease is geographically colocated with specific objects like a well, a hospital, or a river.
In time-series data analysis, researchers have discretized time-series values into several intervals therefore that small fluctuations and value differences can be ignored. The data can be summarized into sequential patterns, which can be indexed to simplify similarity search or comparative analysis.
In image analysis and pattern recognition, researchers have also orderly frequently appearing visual fragments as visual words, which can be used for efficient clustering, classification, and comparative analysis.
Pattern mining has been used for the analysis of sequence or structural data including trees, graphs, subsequences, and networks. In software engineering, researchers have coherent consecutive or gapped subsequences in code execution as sequential patterns that support identify software errors.
Copy-and-paste errors in huge software programs can be recognized by extended sequential pattern analysis of source code. Plagiarized software programs can be recognized based on their substantially identical program flow/loop mechanism.
Frequent and discriminative patterns can be used as primitive indexing mechanism (called graph indices) to provide search large, complex, structured data sets and networks. These provide a similarity search in graph-structured data including chemical compound databases or XML-structured databases. Such patterns can be used for data compression and description.
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