To learn from data, we must be able to understand and manage data in all forms. Data originates from many different sources, and consequently, datasets may differ widely in structure or have little or no structure at all. In this section, we present a high-level classification of datasets with commonly occurring examples.
Based on their structure, or the lack thereof, datasets may be classified as containing the following:
Structured data: Datasets with structured data are more amenable to being used as input to most machine learning algorithms. The data is in the form of records or rows following a well-known format with features that are either columns in a table or fields delimited by separators or tokens. There is no explicit relationship between the records or instances. The dataset is available chiefly in flat files or relational databases. The records of financial transactions at a bank shown in the following figure are an example of structured data:
Financial card transactional data with labels of fraud
Transaction or market data: This is a special form of structured data where each entry corresponds to a collection of items. Examples of market datasets are the lists of grocery items purchased by different customers or movies viewed by customers, as shown in the following table:
Market dataset for items bought from grocery store
Unstructured data: Unstructured data is normally not available in well-known formats, unlike structured data. Text data, image, and video data are different formats of unstructured data. Usually, a transformation of some form is needed to extract features from these forms of data into a structured dataset so that traditional machine learning algorithms can be applied.
Sample text data, with no discernible structure, hence unstructured. Separating spam from normal messages (ham) is a binary classification problem. Here true positives (spam) and true negatives (ham) are distinguished by their labels, the second token in each instance of data. SMS Spam Collection Dataset (UCI Machine Learning Repository), source: Tiago A. Almeida from the Federal University of Sao Carlos.
Sequential data: Sequential data have an explicit notion of "order" to them. The order can be some relationship between features and a time variable in time series data, or it can be symbols repeating in some form in genomic datasets. Two examples of sequential data are weather data and genomic sequence data. The following figure shows the relationship between time and the sensor level for weather:
Time series from sensor data
Three genomic sequences are taken into consideration to show the repetition of the sequences
CGGGTandTTGAAAGTGGTGin all three genomic sequences:
Genomic sequences of DNA as a sequence of symbols.
Graph data: Graph data is characterized by the presence of relationships between entities in the data to form a graph structure. Graph datasets may be in a structured record format or an unstructured format. Typically, the graph relationship has to be mined from the dataset. Claims in the insurance domain can be considered structured records containing relevant claim details with claimants related through addresses, phone numbers, and so on. This can be viewed in a graph structure. Using the World Wide Web as an example, we have web pages available as unstructured data containing links, and graphs of relationships between web pages that can be built using web links, producing some of the most extensively mined graph datasets today:
Insurance claim data, converted into a graph structure showing the relationship between vehicles, drivers, policies, and addresses