Complex Data Objects in Data Mining: What to Know

The complex data types in data mining require advanced data mining techniques. Sequence data, such as time series, symbolic sequences, and biological sequences, are some examples of complex data types.

For data mining of these complex data types, additional preprocessing steps are required. Keep reading and find out more

Complex Data Types in Data Mining

The 12 different types are listed below.

1. Time-Series Data Mining

Data is measured as a long series of numerical or textual data at regular intervals of one minute, one hour, or one day in time-series data. Data from the stock markets, academic research, and medical records are all used for time-series data mining. It is not possible to find data in time series mining that exactly matches the given query. We use the similarity search method, which identifies data sequences that are similar to the provided query string. A given query string is used to find subsequences that are similar to it using the similarity search method, which uses subsequence matching. Dimensionality reduction of complex data is done in order to convert time-series data into numerical data, which is then used in similarity search.

2. Sequential Pattern Mining in Symbolic Sequences

Long nominal data sequences that dynamically change their behavior over time are the building blocks of symbolic sequences. Online customer shopping sequences and experimental event sequences are two examples of symbolic sequences. Sequential mining is the process of extracting symbols from sequences. A sequence subsequence that appears more frequently in a collection of sequences is known as a sequential pattern. so it finds the most frequent subsequence in a set of sequences to perform the mining. To determine the frequent subsequence, numerous scalable algorithms have been developed. To mine the multidimensional and multilevel sequential patterns, there are additional algorithms.

3. Data Mining of Biological Sequences

Long nucleotide sequences make up biological sequences, and data mining of biological sequences is necessary to uncover the characteristics of human DNA. Data mining’s first step in comparing the alignment of biological sequences is biological sequence analysis. Only when the nucleotide (DNA, RNA) and protein sequences of two species are similar do we say that the two species are similar. Nucleotide sequence similarity is evaluated during the data mining of biological sequences. When comparing two sequences, the degree of homology that can be determined by aligning the nucleotide sequences is crucial.

By identifying similar sequences with long subsequences, two or more input biological sequences can be aligned. Also compared and aligned are the amino acid sequences, which are also known as protein sequences.

4. Statistical Modeling of Networks

A network is a group of nodes connected by edges that represent the connections between different data objects. Each node in a network represents a piece of data. A network is homogeneous if every node and link connecting those nodes belongs to the same type. Examples include friend networks and web page networks. Health care networks (which link various parameters like doctors, nurses, patients, and diseases together in the network) are examples of heterogeneous networks because the nodes and the links that connect them are of different types. The network can be further mined using graph pattern mining to extract knowledge and practical patterns.

5. Graph Pattern Mining

Approaches based on pattern growth and those based on Apriori can both be used for graph pattern mining. The set of closed graphs and its subgraphs can be mined. A closed graph, g, is one that lacks a supergraph with the same number of supports as g. Graph Pattern Mining is used with a variety of graph types, including dense, frequent, and coherent graphs. By imposing user constraints on the graph patterns, we can increase the mining efficiency as well. In a graph, there are two types. graphs that are homogeneous have nodes or links that belong to the same type and share characteristics. There are various kinds of nodes and links in heterogeneous graph patterns.

6. Mining Spatial Data

The geospatial data that is kept in sizable data repositories is known as spatial data. The spatial data is represented in “vector” format and geo-referenced multimedia format. Large geographic data warehouses are used to build spatial databases by combining geographic information from various sources. we can construct spatial data cubes that contain information about the spatial dimensions and measures. For the purpose of analyzing spatial data, OLAP operations can be applied to spatial data. Spatial databases, spatial data warehouses, and other geospatial data repositories are all used in spatial data mining. Geospatial data mining reveals information about the locations. The preprocessing of spatial data entails a number of operations, including spatial clustering, spatial classification, spatial modeling, and outlier detection in spatial data.

7. Mining Cyber-Physical System Data

A graph or network of data can be built to mine data from cyber-physical systems. A heterogeneous network of numerous connected nodes that store patient data or other medical information is known as a cyber-physical system (CPS). The CPS network’s links show how the nodes are connected to one another. cyber-physical systems store dynamic, inconsistent, and interdependent data that contains spatiotemporal information. Through the use of real-time calculations and analysis, mining cyber-physical data uses the situation as a query to gain access to information from a sizable information database and triggers responses from the CPS system. In cyber-physical data streams, cyber-physical networks, and the processing of cyber-physical data, rare-event detection, anomaly analysis, and the integration of stream data with real-time automated control processes are all necessary for CPS analysis.

8. Mining Multimedia Data

Image, video, audio, website links, and linkages are examples of multimedia data objects. From multimedia databases, multimedia data mining seeks out intriguing patterns. As part of this, digital data is processed to carry out operations like image processing, image classification, video and audio data mining, and pattern recognition. Due to the ability to analyze data from most social media platforms, including Twitter and Facebook, and identify interesting trends and patterns, multimedia data mining is quickly becoming the most fascinating research field.

9. Mining Web Data

Web mining is necessary to extract important patterns and information from the Web. Web content mining examines information from numerous websites, including web pages and multimedia information like images. Web mining is done to comprehend the content of web pages, unique users of the website, unique hypertext links, web page relevance and ranking, web page content summaries, time that the users spent on the particular website, and comprehend user search patterns. The best search engine is also discovered by web mining, along with the search algorithm it employs. Therefore, it aids in increasing search efficiency and identifies the best search engine for users.

10. Mining Text Data

Data mining, machine learning, statistics, and natural language processing are all subfields of text mining. The majority of the information we encounter every day is text-based and can be found in things like news articles, technical papers, books, emails, and blogs. Using text mining techniques like sentiment analysis, document summaries, text categorization, and text clustering, we can extract high-quality information from text. To extract useful information from the text, we use machine learning models and NLP strategies. This is accomplished by identifying the hidden trends and patterns using techniques like statistical language modeling and statistical pattern learning. Stemming and lemmatization are two preprocessing methods that must be used on the text before text mining can be performed in order to turn the textual data into data vectors.

11. Mining Spatiotemporal Data

Spatial and temporal data is information that relates the two. Interesting patterns and information are extracted from spatiotemporal data by spatial temporal data mining. Finding the value of land, determining the age of rocks and precious stones, and forecasting weather patterns are all made possible by spatial temporal data mining. GPS in mobile phones, timers, web-based map services, weather services, satellite, RFID, and sensor are just a few of the many useful applications of spatial temporal data mining.

12. Mining Data Streams

Data that can change dynamically and that is noisy, inconsistent, and contains multidimensional features of various data types is referred to as stream data. So, NoSql database systems are used to store this data. The difficulty in successfully mining stream data comes from the sheer volume of the data, which is very high. We must carry out tasks like clustering, outlier analysis, and the real-time detection of rare events in data streams while mining the data streams.

Read More:

• What Is Classification In Data Mining?
• What Is Regression In Data Mining?

Complex Data Objects With Deep Components

Internal tables with a deep row type are a common example of a data object with many deep components. In this case, it is important to prevent the management costs (in the form of memory) for references and headers from becoming disproportionately high in comparison to the actual data content.

For complex data objects with relatively little data content, three basic cases can be distinguished:

1. Complex data objects with a sparse fill level
   If a complex deep data object has a lot of deep components, the majority of which are initial, that object is said to be sparsely populated. Unless the component already points to a header, a deep initial component like this requires 8 bytes of memory.
2. Complex data objects with a duplicative fill level
   If a complex deep data object contains numerous deep components, and the majority of those deep components refer to the same data, then there is a duplicative fill level. This type of component only contributes to the memory requirements of its references while sharing the dynamic memory. This is accomplished through sharing for dynamic data objects.
3. Complex data objects with a low fill level
   If a complex deep data object has numerous deep components that point to various objects, strings, or internal tables, but the objects only use a tiny amount of memory or are empty, the object has a low fill level.

Deep data objects that have a fill level that is moderately sparse, duplicative, and not too low are typically safe.

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