Text Mining and Data Mining: Understanding the Differences

This article discusses the differences between text mining and data mining. It explains how text mining is used to analyze unstructured data sets and the importance of structuring the data. It also explores the various approaches used in data mining to search for relationships in structured data. The article provides examples and highlights the benefits of using these techniques in decision support systems.

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Decision Support Systems
IT-445

Pg. 1 ReferencesReferences
Question One
What is Text mining? Differentiate it from Data mining.
Answer
Text mining is a method of gathering and analyzing huge unstructured data
sets. Identification of patterns, topics, keyword and other attributes helps in better
analyzing of the data that are being collected. Implementation of big data in the regular
process ensures that the usage of the terminology is affected in a positive manner. In
case of unstructured data set, deep learning algorithm is used (Weiss, Indurkhya and
Zhang 2015). With the help of this terminology, better analysis can be performed in a
better manner. In the initial stage, the process starts with identifying and retrieving of
relevant sets of text data for better analyzing. After his process algorithm is used for
proper categorizing, summarizing and organizing of data. Using of analytical model for
proper understanding of the ID concepts patterns and other attributes. Preparing f data
visualization and dashboard for sharing of results are also performed.
The main difference in between text mining and data mining is that in case of
using of the text mining, structuring of the unstructured data is important, whereas in
case of using data mining process, a spectrum of different approaches are used for
searching of the relationships of data (Sharda, Delen and Turban 2015). In the data
retrieval process, standard data mining process leads to discovering of the lexical and
syntactic features whereas the technique that is used by the data mining process
includes revealing of business patterns in numerical data.
Learning
Outcome(s): LO3
Use some DSS
and demonstrate
the database
working with DSS
and statistical
models.
1 Marks

Pg. 2 ReferencesReferences
Question Two
Crossover, Mutation and Replication are the three genetic operators used in Genetic
Algorithm for the generational evolution into an optimum solution.
Briefly explain the role of each of these and conclude which one is best suited to
introduce more diversity in the population.
Answer:
Crossover: Crossover happens when proper alignment of the homologous
chromosomes are aligned with each other.in case of cross over the chromatids from the
two variable chromosomes exchanges the segments. Chromatids from the two
homologous chromosomes get into contact in an equivalent length. After this section
the chromatids gets separated after completing the processing of the segment
exchanging.
Mutation: Mutation takes place in case the DNA gene is damaged or alteration
takes place in an unusual manner. In case of mutation the entire system that is carried
out includes alteration in the messages that are sent via the process. mRNA that is
transcribed for from the muted gene will now transfer the altered message. The
polypeptide that is used for translation of the mRNA will be having an altered
sequence of amino acids (Hatkevich et al. 2016).
Replication: Replication is a process that will insist in developing of the
identical replicas of the original DNA molecule. This process happens in the
processing of biological inheritance. The helix of DNA are separated during the
replication process (Nosek and Errington 2017). Unwinding of the DNA from the
origin and proper synthesizing will incur proper synthesis process.
Crossover will be suited in the best manner for increasing the diversity in
generational evolution.
Learning
Outcome(s): LO2
Analyze various
industrial
applications of
DSS and their
limitations.
2 Marks

Pg. 3 ReferencesReferences
Question Three
Differentiate Web Structure mining and Web Usage mining. Discuss in detail.
Answer
Web structure mining is used for proper generation of the structural summary
of web sites and web page. Discovering of the structure of web content are also
performed by the web structure mining (Tyagi and Gupta 2018). Web structure ensures
the fact that the management of the structure management performs proper revealing
of the schema of the web page. This process also facilitates proper introducing of
database techniques. This process also helps in proper analyzing of the nodes as per
the connection structure. This process will get performed as per the data that are
gained by the graph theory. This helps in better management of the structure of the
website.
Web data mining is used for processing of usage of data. Different techniques
and algorithms are extracted from information directly as per the web documentation
and services. Extracting of details from web contents, hyperlinks and server logs are
also the main functionalities of Wen usage mining (Tan 2018). With the help of web
usage mining extracting patterns will get performed in a better manner for gaining
better information from the server logs. This process also helps in gaining of proper
patterns and perform clicking of items that are present on the sites.
Learning
Outcome(s): LO2
Analyze various
industrial
applications of
DSS and their
limitations.
LO3: Use some
DSS and
demonstrate the
database working
with DSS and
statistical models.
1.5 Marks

Pg. 4 ReferencesReferences
Question Four
What is knowledge engineering? Give examples of Synchronous and Asynchronous
Tools for Indirect Support of Decision Making.
Answer
Knowledge engineering can be defined as a field of artificial intelligence that
will provide high benefits regarding the implementation of the artificial intelligence.
This process helps in better creation of rules that will be created in order to imitate the
functioning of human activities (Quezada-Sarmiento et al. 2016). The library of
collateral knowledge will help in better problem solving of the diagnosed system.
Knowledge engineering can be processed as per the expertising of the problems.
Explicating of data management as per the processing of decision making gets
benefitted.
Examples of tools that are used for decision making and are asynchronous in
nature is Apace Software Foundation. With the help of the Apache Software
foundation, efficient meetings to lead teams are beneficial.
Example of tools that are used for decision making and are synchronous in
nature is Interpersonal Communication tool.
Learning
Outcome(s): LO1
Describe the
structure of
Decision Support
Systems (DSS)
and their
services.
1.5 Marks

Pg. 5 ReferencesReferences
References
Hatkevich, T., Kohl, K.P., McMahan, S., Hartmann, M.A., Williams, A.M. and
Sekelsky, J. (2017). Bloom syndrome helicase promotes meiotic crossover patterning
and homolog disjunction. Current Biology, 27(1), pp.96-102.
Nosek, B.A. and Errington, T.M. (2017). Reproducibility in cancer biology: Making
sense of replications. Elife, 6, p.e23383.
Quezada-Sarmiento, P.A., Enciso-Quispe, L.E., Garbajosa, J. and Washizaki, H. (2016,
July). Curricular design based in bodies of knowledge: Engineering education for the
innovation and the industry. In 2016 SAI Computing Conference (SAI) (pp. 843-849).
IEEE.
Sharda, R., Delen, D. and Turban, E. (2015). Business Intelligence and Analytics:
Systems for Decision Support. (10th ed.)
Tan, P.N. (2018). Introduction to data mining. Pearson Education India.
Tyagi, N. and Gupta, S.K. (2018). Web structure mining algorithms: A survey. In Big
Data Analytics (pp. 305-317). Springer, Singapore.
Weiss, S.M., Indurkhya, N. and Zhang, T. (2015). Fundamentals of predictive text
mining. Springer.