Clustering Techniques Within Service Sector

2017 ◽  
pp. 74-87 ◽  
Author(s):  
İbrahim Yazici ◽  
Ömer Faruk Beyca ◽  
Selim Zaim
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Service Sector ◽  
Data Availability ◽  
Management Tool ◽  
Clustering Method ◽  
Data Mining Technique ◽  
Clustering Techniques ◽  
Group A

Due to big data availability in markets recently, processing and making predictions with data have been becoming more difficult, and this difficulty has been affecting management decisions. As a result, competitiveness for companies are related to analyze and utilize big data in order to achieve company targets. Transforming big data into business advantage has become a vital management tool across all industries. There are many data mining techniques that are being applied to plenty of problems. One of the frequently utilized data mining technique is clustering method. Clustering techniques aim to group a set of objects in clusters that more similar objects are in the same cluster. Main utilization aim of clustering techniques is segmenting or clustering or grouping objects. Clustering techniques and their utilization within service sector by aim of clustering technique and their methodologies are presented. Energy, social media and bank sectors are found that the mostly user of clustering techniques within service sector for segmenting customers based on searched papers.

scholarly journals Recent trends in big data using hadoop

2019 ◽  
Vol 8 (1) ◽  
pp. 39
Author(s):  
Chetna Kaushal ◽  
Deepika Koundal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Nearest Neighbor ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Clustering Techniques ◽  
Recent Trends ◽  
K Nearest Neighbor Algorithm

<span>Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbor algorithm is discreetly chosen among them and described along with an example. </span>

A Survey of Social Media, Big Data, Data Mining, and Analytics

2018 ◽  
Vol 03 (03) ◽  
pp. 1850003 ◽  
Author(s):  
Jared Oliverio
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Environmental Education ◽  
Family Businesses ◽  
Large Percentage ◽  
Specific Behavior ◽  
Social Media Data ◽  
Media Applications ◽  
Media Data

Big Data is a very popular term today. Everywhere you turn companies and organizations are talking about their Big Data solutions and Analytic applications. The source of the data used in these applications varies. However, one type of data is of great interest to most organizations, Social Media Data. Social Media applications are used by a large percentage of the world’s population. The ability to instantly connect and reach other people and companies over distributed distances is an important part of today’s society. Social Media applications allow users to share comments, opinions, ideas, and media with friends, family, businesses, and organizations. The data contained in these comments, ideas, and media are valuable to many types of organizations. Through Data Mining and Analysis, it is possible to predict specific behavior in users of the applications. Currently, several technologies aid in collecting, analyzing, and displaying this data. These technologies allow users to apply this data to solve different problems, in different organizations, including the finance, medicine, environmental, education, and advertising industries. This paper aims to highlight the current technologies used in Data Mining and Analyzing Social Media data, the industries using this data, as well as the future of this field.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Machine Learning for Business Analytics

2021 ◽  
pp. 232-256
Author(s):  
Kağan Okatan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Business Analytics ◽  
Business Intelligence Systems ◽  
Long Time ◽  
Rules Of The Game

All these types of analytics have been answering business questions for a long time about the principal methods of investigating data warehouses. Especially data mining and business intelligence systems support decision makers to reach the information they want. Many existing systems are trying to keep up with a phenomenon that has changed the rules of the game in recent years. This is undoubtedly the undeniable attraction of 'big data'. In particular, the issue of evaluating the big data generated especially by social media is among the most up-to-date issues of business analytics, and this issue demonstrates the importance of integrating machine learning into business analytics. This section introduces the prominent machine learning algorithms that are increasingly used for business analytics and emphasizes their application areas.

Big Data and Clustering Techniques

2020 ◽  
pp. 1-9
Author(s):  
Jayashree K. ◽  
Chithambaramani R.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sensor Data ◽  
Log Data ◽  
Clustering Techniques ◽  
Research Challenges ◽  
The Future ◽  
Process Operations ◽  
Future Direction

Big data has become a chief strength of innovation across academics, governments, and corporates. Big data comprises massive sensor data, raw and semi-structured log data of IT industries, and the exploded quantity of data from social media. Big data needs big storage, and this volume makes operations such as analytical operations, process operations, retrieval operations very difficult and time consuming. One way to overcome these difficult problems is to have big data clustered in a compact format. Thus, this chapter discusses the background of big data and clustering. It also discusses the various application of big data in detail. The various related work, research challenges of big data, and the future direction are addressed in this chapter.

Purchaser's Optimistic Response to Social Media Advertisements—A Data Mining Approach

2019 ◽  
Vol 16 (2) ◽  
pp. 664-668
Author(s):  
S. Magesh ◽  
S. Vijayalakshmi
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Clustering Techniques ◽  
Data Mining Approach ◽  
Online Social Media ◽  
Online Advertisements ◽  
Online Advertisement ◽  
Selection Of ◽  
Significant Factors

The paper aspires at discovering the most indispensable factors persuading customer reactions and purchasing commodities after observing online advertisements of social media and recognizing the distinctiveness of clusters of Purchaser having the optimistic reaction, over and above of buying customer clusters after analyzing online advertisement in social media. The selection of attribute and clustering techniques are incorporated in the analysis of data to find significant factors and target customer clusters correspondingly through data mining approach. It has been identifies that there is a strapping correlation between the advertisement being clicked on social media and the fulfillment with commodities, and amidst purchasing commodities online and saving information for supplementary deliberations. The findings also points out the characteristics of product and price Conscious clusters for Purchasers' reaction and procuring after seeing online social media advertisement.

scholarly journals Twitter’s User Opinion About Master and Doctoral Degrees: A Model of Sentiment Comparison

2020 ◽  
Vol 14 (4) ◽  
pp. 429
Author(s):  
Victor Wiley ◽  
Thomas Lucas
Keyword(s):  
Data Mining ◽  
Public Opinion ◽  
Doctoral Degree ◽  
Doctoral Degrees ◽  
Data Mining Technique ◽  
Dominant Type ◽  
Master Degree ◽  
Mining Technique ◽  
Group A ◽  
Group B

This paper examines the opinion of student candidate about their plan to study further to master degree (S2) and doctoral degree (S3). There is lack of approach in finding public opinion about the interest of student candidate in continuing study to higher level such as master degree or doctoral degree. Through this paper, the Twitter’s user opinions are extracted using certain data mining technique to find out three sentiment types (negative, neutral, and positive) by taking the most dominant type of emotions (i.e., anger, anticipation, love, fear, joy, sadness, surprise, trust). The dataset is divided into two groups of Twitter’s users. Both datasets represent group A those opinion is about continuing study further to master degree versus group B whose continuing to doctoral degree. The groups are then divided into three types of sentiment statements about master degree versus doctoral degree. The first group is their sentiment about continuing study further to master degree with the result: (a) 109 negative tweets, 1683 neutral tweets and 131 positive tweets. For the second group (e.g., student’s sentiments about continuing to doctoral degree), it has results: (a) 421 negative tweets, 7666 neutral tweets and 1805 positive tweets. The data are tested to give accuracy value of 85%. The result of this sentiment analysis is useful as a reference for universities to understand the development of sentiments (opinion) from Twitter’s users and help the institutions to improve their reputation and quality

scholarly journals Detection of Crimes Using Unsupervised Learning Techniques

2020 ◽  
Vol 2 (1) ◽  
pp. 8-11
Author(s):  
R. Buli Babu ◽  
G. Snehal ◽  
Aditya Satya Kiran
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Similar Data ◽  
Data Mining Technique ◽  
Clustering Techniques ◽  
Mining Technique ◽  
Learning Techniques ◽  
Object Based

Data mining can be used to detect model crime problems. This paper is about the importance of datamining about its techniques and how we can easily solve the crime. Crime data will be stored in criminal’s database.To analyze the data easily we have data mining technique that is clustering. Clustering is a method to group identicalcharacteristics in which the similarity is maximized or minimized. In clustering techniques also we have different typeof algorithm, but in this paper we are using the k-means algorithm and expectation-maximization algorithm. We areusing these techniques because these two techniques come under the partition algorithm. Partition algorithm is oneof the best methods to solve crimes and to find the similar data and group it. K-means algorithm is used to partitionthe grouped object based on their means. Expectation-maximization algorithm is the extension of k-means algorithmhere we partition the data based on their parameters.

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