Negative patterns are the patterns that are composed of elements that behave and correlate negatively. The patterns are also referred to as frequent patterns because they are found easily. Normally these patterns are not significant as opposed to the positive patterns. However, the patterns play a significant role in identifying the essential structures of data in a data mining assessment. The space for mining is much greater under the negative essential data structure patterns.

As such, these types of data structures have created more room and easy time in assembling and classifying the data objects. The negative patterns are assessed by looking at the generic algorithm of the data that has been given. The generic algorithm has been proven scientifically to be the most efficient method of finding these algorithms. There are many types of data that fall under negative patterns. For example, in the sales data of watches, the diamond watches are an example of frequent data. Therefore, there are many types of negative patterns; however, the nature of the data dictates the number.

Rare data patterns are one of the most common types of data structures in data mining. The structures are the rarest to find, but they are the major objects of interest. More especially, when the researcher is dealing with these particular objectives of interest, they must classify their data from the most significant to the least significant. As such, they can determine the objects of interest by considering the features of all the data elements. As a result, the null invariance object is the best classifier for these types of objects.

These types of objects are essential for assessment and analysis in data mining and scrapping. The unusual behavior of the data is the most critical feature that highlights the special features of interest. The best example is the sales of Coca-Cola products in a store. Therefore, the frequency of the customers is the basic data of interest.