Top PDF Analysis of Customer Behavior using Clustering and Association Rules

Analysis of Customer Behavior using Clustering and Association Rules

Analysis of Customer Behavior using Clustering and Association Rules

4.2 Apriori Algorithm All data that are recorded in the transaction database is fed as input for the Apriori algorithm, which generates rules based on the support and confidence measures[r]

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Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

Optimized Incremental Mining of Customer Buying Behavior using Temporal Association Rules

Various methodologies have been used for analyzing customer buying behavior. In [12], analysis of customer behavior is performed by a semi-supervised learning method from a recording of their body movements. The method proposed in [13] introduces a cognitive dissonance to explore the reasons which is able to create cognitive dissonance among several buying behaviors of the buyers. In [14], Chang et al. proposed a new GRFM (Global RFM) mannequin that employs a constrained clustering (CC) method to analyze purchasers' consumption behavior based on the three variables namely, consumption interval, frequency and cash amount. The method proposed in [15] offered a passive RFID (Radio Frequency Identification) tags to detect and record the behavior of customers (i.e., how customers browse stores, on which clothes they pay attention to, and which garments they commonly pair up). In [16], the author provided a real-time approach for performing the purchasing behavioral analysis by utilizing the depth cue supplied within the 3D pose of the customer. The K-means clustering algorithm proposed in [17] contributes to the mining of customer purchasing behavior by accumulating the information from e-commerce websites.
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Prediction of Criminal Suspects Based on Association Rules and Tag Clustering

Prediction of Criminal Suspects Based on Association Rules and Tag Clustering

DOI: 10.4236/jsea.2019.123003 37 Journal of Software Engineering and Applications relatively high crime potential and improve crime prevention implementation. Extensive research has been conducted in China on ARM-based crime mining. Based on fuzzy set and rough set theory, Chen and Yu [11] [12] employed ARM to quantitatively analyze a criminal dataset, make deductions, and extract rules, providing theoretical guidance for crime prevention. ARM has gained wide- spread attention and application in the fields of criminal portraits and criminal forensic analysis [13]. Moreover, ARM has been widely used in crime research such as crime investigation [14], criminal suspect analysis [15], criminal beha- vior analysis [16], and reoffending [17] [18]. In view of the temporal and spatial characteristics of crime data, many studies have proposed a number of improved ARM algorithms such as spatio-temporal association rules [19] [20], cluster as- sociation rules [21], and data cube-based association rules [22], as well as other improved algorithms such as incremental association rules [23].
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Decision making using Multi Inference-LDA          Algorithm

Decision making using Multi Inference-LDA Algorithm

The Latent Dirichlet allocation (LDA) model is important to emphasize that an assumption of exchangeability is not equivalent to an assumption that, the random variables are independent and identically distributed. But they are conditionally independent and identically distributed. Latent Dirichlet Allocation (LDA) is a generative probabilistic model of a corpus. The basic idea is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words. An empirical evaluation of LDA is used in problem domains like document modeling, document classification, and collaborative filtering [1]. The narrow concept of data mining is one critical step of knowledge discovery, an important procedure of drawing useful schemas or building model [7]. Financial analysis is to find out the economy meaning of accounting data in order to understand the running performance and financial position of one company, which helps investors and creditors with their decision making [4]. An analysis model of financial statements based on data mining methods, such as clustering, association rules and decision making tree work together to step by step go into deeper analysis of existing financial statements, during which a annual assets structure statement is worked out. In order to go deep and completely into analyzing the financial data of companies, They use OLAP tool of data mining, build super-cubes according to their own needs, examine or analyze data from multiple dimensions[4].
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Behavior-Based Clustering and Analysis of Interestingness Measures for Association Rule Mining

Behavior-Based Clustering and Analysis of Interestingness Measures for Association Rule Mining

Ranking rules can be made in association rules as per the support of interestingness measure like confidence, lift ,leverage ,conviction this normally uses the confidence as the ranking measure for generated associated rules .this highest confidence can be moved to first rank is series and lowest confidence can be moved to last rank in series. the number of rule generated is depend on simulation ranking always get advanced as the meaningfull rules get sorted in set and domain knowledge is to be get sorted out in ranking order.

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Design of Flexible Mining Language on Educational Analytical DataBase

Design of Flexible Mining Language on Educational Analytical DataBase

These types of data mining query language learning and knowledge extraction are to perform search operations on databases. Here's a broader perspective, we examined the DMQL data mining language. This language education data mining capabilities in relational databases have been on several levels, flexible and interactive user interface. The language in question is intended as a summary of some data mining, data mining of association rules, classification, clustering and finding specific patterns. The language of the user interface, flexible, capable and multi- level debugging and searching facilities such as tables, charts, manuals and dynamic selection puts at their disposal. This language research team from Simon Fraser University in Canada was designed by Dr. Hahn. This language will be implemented under DBMiner system capable of efficient extraction of a variety of laws and knowledge to effectively and efficiently be available. Other features include integrated security systems; graphical user interface is very powerful and relevant tool to represent knowledge, power mining classification rules, association, and cluster, high efficiency and the powerful of responses. DMQL questions derived from the SQL language, training data can be used to analyze it. Types of knowledge that can be extracted by the language of the classification rules, clustering analysis method is dependent on the exchange of information and technology transfer is possible by language. DMQL question the decision tree classification language is implemented in the model tree, each node represents a feature value and each split test, display test output classes are the leaves of the tree. DMQL questions derived from the SQL language, it can be used to analyze training data. Types of knowledge that can be extracted by the language of the classification rules, clustering analysis method is dependent on the exchange of information and technology transfer is facilitated by the language. DMQL question the decision tree classification language is implemented in the model tree, each node represents a feature value and each split test, display test output classes are the leaves of the tree.
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Application of Data Mining with Association Rules to Review Relationship between Insured, Products Selection and Customer Behavior

Application of Data Mining with Association Rules to Review Relationship between Insured, Products Selection and Customer Behavior

Data mining is a new technology that is very useful to help insurance companies to find very important information from business data as their main asset. Data mining can predict trends and traits of business behavior that are very useful to support important decision making in determining strategies that can guarantee the continuity of their insurance business. Automated analysis carried out by data mining exceeds that carried out by traditional decision support systems that have been widely used in general, thus encouraging the author to take research on the application of Data Mining with Association Rules to see the relationship between Insured, Product Selection and Customer Behavior.
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Performance and Analysis Public Options Based on Using K- Means Algorithm T. G. Babu* 1, R. Sharmila2

Performance and Analysis Public Options Based on Using K- Means Algorithm T. G. Babu* 1, R. Sharmila2

Along with the development of association rules and clustering- two mining technologies, research in clustering technology based on association rules has also become more and more. Firstly, researchers had many improvements in the similarity computing methods mainly through the association rules technology. Literature [3] has given a new association rule method. It measures the distance between the rules by commodity information classification information. The entire process scanned primitive data sets only once, thus it saves time. Literature proposes one similarity computing algorithm based on the words ”relational degree”. This algorithm obtains good clustering results. In addition, the frequent item set is the foundation of association rules, so clustering technology based on the frequent item set had many improvements. Literature [5] has improved text clustering method based on frequent item-set in WEB documents through the cross link chart instead of traditional calculating methods obtaining the frequent item-set. To solve the two limitations K - means algorithm has, Longhao, Fengjianlin, ET propose R-means algorithm
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Search Aspects and Participant Major Datasets Using Cluster Mining

Search Aspects and Participant Major Datasets Using Cluster Mining

Abstract: The data mining of association rules between items in a large database is an essential research aspect in the data mining fields. Discovering these associations is beneficial to the correct and appropriate decision made by decision-makers. Fast retrieval of the relevant information from databases has always been a significant issue. Clustering is a main task of exploratory data analysis and data mining applications. The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information. The evaluation of competitiveness always uses the customer opinions in terms of reviews, ratings and abundant source of information’s from the web and other sources. We include platform and framework for managing and processing large data sets. We also discuss the knowledge discovery process, data mining, and various open source tools. . User generated text data is intrinsically noisy, with misspellings, informal language, and digressions. Because of the many variations in spelling and expression,
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A Survey of the Application of Association Rules in Library Services

A Survey of the Application of Association Rules in Library Services

This article summarizes the current research hotspots based on association rules reader behavior analysis, personalized recommendation, resource allocation and mobile library application[r]

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ON MODELING TRACES IN A COMPUTING ENVIRONMENT FOR HUMAN LEARNING BASED 
INDICATORS

ON MODELING TRACES IN A COMPUTING ENVIRONMENT FOR HUMAN LEARNING BASED INDICATORS

Appropriate strategy is an absolute must in the tight business competition of today. Many methods are used in business to predict and process data so that information and knowledge needs of the business can be obtained. Data mining is often used as a solution to this problem. Data mining is used to explore the hidden information in the data warehouse, and able to show a pattern of behavior of customers in their daily life associated with the company. K-Means and association rule are two techniques of the many techniques contained in the data mining algorithm that can be used as a basis for analysis to improve profitability. Keywords: Data Mining, CRM, K-Means, Association Rules
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IJCSMC, Vol. 8, Issue. 4, April 2019, pg.177 – 181 The Data Mining Model that Predicts the Customer Decision-Making in Buying a Car

IJCSMC, Vol. 8, Issue. 4, April 2019, pg.177 – 181 The Data Mining Model that Predicts the Customer Decision-Making in Buying a Car

Abstract: For the last few of decades, India has become one of the largest country in the world in the adaptation and establishment of the automobile industry which growing steadily each year. That is why the decision-making and behavior of a customer become a point of interest. The decision of buying a car constitutes a unique type of buying behavior. This is something different in comparison to the buying or shopping of the essential needs of the life. The main objective to this prediction is to advance the knowledge of what are the factors that influences the customer’s choices of the car stores. More specifically, the main focus has been on how different situations (e.g. type of buying) influence the choice of the car stores. The frequent item sets are mined and associated from the market basket database using the efficient algorithm and hence the association rules are generated. The decision tree can be constructed using Linear Regression algorithm and Random Forest Classification algorithm. Then at last the prediction made by the both linear regression algorithm and random forest classification algorithm is compared for the best results.
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Two Recommendation System Algorithms Used SVD And Association Rule On Implicit And Explicit Data Sets

Two Recommendation System Algorithms Used SVD And Association Rule On Implicit And Explicit Data Sets

Our new proposed technique used to increase the accuracy of predicted items will be recommended to users also solve the sparsity problem by using the merging between implicit and explicit data. Our data sets are part of million song data sets is last.FM datasets [12] used for the research paper [13]. Our new techniques are best compared to the basic collaborative filtering techniques and new k-means and SVD algorithm. The main advantage of our proposed algorithm is how to find the correlation between items in a spare data this be solved by using implicit data (Songs played, number of play counts to a specific song, play ratio for certain category of songs and tagging information). Also, a similar behavior pattern can compute easily with our new algorithms this led to a good recommendation. The main idea we solve in this technique where data are spare in this case users don’t have any co-played music and we need to find similar preferences on pop music. However, such a result is not true in more sparse rating data. Even though both users do not have any co-played music, both are fans of pop music. Thus, we should consider them to be similar in the case that they are sharing a very similar preference for pop music. We will compute the similarity between users on playing songs groups by applying the clustering techniques and association rules to find hidden items between active users, or users interested in items available on the system. figure 3 show new recommender system steps. We will discuss proposed algorithms Phase 1: Pre-processing Reducing data dimensionality for the rule mining part
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Enhancing the Analysis of Customer Behavior in Supermarket Through Map Reduce

Enhancing the Analysis of Customer Behavior in Supermarket Through Map Reduce

The Hadoop Distributed File System (HDFS) is used for storage. In short, HDFS provides a distributed architecture for extremely large scale storage, which can easily be extended by scaling out. When a file is stored in HDFS, the file is separated into uniformed size blocks. The size of block can be customized or the predefined one can be used. In this project, the customer dataset are stored in the HDFS. The dataset contains a lot of customer records with respect to their interest and purchases made by them. Also, the output file containing decision rules is written into HDFS for recommendation.
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Hypergraph clustering model based association analysis of DDOS attacks in fog computing intrusion detection system

Hypergraph clustering model based association analysis of DDOS attacks in fog computing intrusion detection system

form a database of security logs on the fog nodes to rec- ord the situation where the fog nodes are attacked. (2) The cloud server monitors and analyzes the situation that the fog node suffers from DDoS in real time. The behavior and attempts of the intruder can be described through the overall monitoring and data mining of the fog cluster. (3) The most appropriate intrusion response is made to the intruder’s behavior for the results of the detection and the conclusion of the information analysis. The first part and the second part have been studied in Ref. [9] and Ref. [10] among them. The second part is the focus in this paper: Correlation analysis of frequent items is performed for statistical data of fog node intru- sion detection. The highlighted portion is the location of the study in the FC-IDS as shown in Fig. 4.
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A Study on Customer Segmentation for E Commerce Using the Generalized  Association Rules and Decision Tree

A Study on Customer Segmentation for E Commerce Using the Generalized Association Rules and Decision Tree

Through analysis and comparison of the traditional model, this paper proposes to build a new integrated customer segmentation model on the e-commerce website: First, build models out of [r]

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Exploring Consumer Behavior: Use of Association Rules

Exploring Consumer Behavior: Use of Association Rules

I Have One Favorite Store Where I Do My Regular Shopping. I Go Elsewhere Only Exceptionally. In general, customers who picked this option have a lot in common with customers who have one favorite retail chain; however, they seem to be less demanding as we can see in Tab. I. It may be important to note that their share in sample is higher than of those who are retail chain loyal. What really matters to them is an easy orientation inside the store and easy location of merchandise. A habit plays a great role in their shopping behavior. As the found best rules confi rm, also for this category of customers provided quality of merchandise and freshness of products play an important role when choosing their outlet for a purchase of meat products, their level is very close to the mean of the whole sample. The analysis has not provided any category specifi c rule, but the results suggest that the respondents for whom the quality of merchandise is important also stress freshness of products, they also
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A Survey of Cyclic Association Rule

A Survey of Cyclic Association Rule

However the algorithms used for association rule mining do not consider the temporal patterns in customer shopping behavior. For example consider association rules of the form {turkey ->pumkin pie} or {plum cake -> Christmas decorations} which apply during periods close to Thanksgiving and Christmas. These association rules might be infrequent for the rest of the year and therefore do not cross the minimum support threshold required for them to appear in the frequent items sets and henceforth in the association rules discovered by the mining system. Also, if they do appear in the association rules determined by our mining system, these association rules are misleading as they are not valid for all time periods. Therefore, taking temporality into consideration is important for deriving more robust association rules.
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Analysis of Customer Churn by Big Data Clustering

Analysis of Customer Churn by Big Data Clustering

Yi Wang, Qixin Chen et al. [3] proposed the clustering of electricity consumption behavior dynamics approach for analyzing electricity consumption of individual customer to enhance the load serving. Symbolic aggregate approximation is used to minimize the storage space by reducing the dimension of the data set. This is done by transforming the load curves to symbolic strings using piecewise aggregate approximation (PAA). Then to model the dynamic of electricity consumption, time-based Markov model is used. Markov model is used to predict the future consumption of electricity from the current state of use of electricity.The customers with similar behavior are classified into groups byclustering technique by Fast Search and Find of Density Peaks (CFSFDP).Kullback–Liebler distance is used to measure the distance between any two random consumption of a particular customer based on that the customer’s cluster group is decided. Big data is handled with ease as CFSFDP uses divide-and-conquers technique.
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Big Data Platform Access Control Rule Generation Method Based on Data Mining

Big Data Platform Access Control Rule Generation Method Based on Data Mining

(1) When the total number of control rules is small, the approval rate is high and it is easier to pass all the rules. The reason for this phenomenon is that when there are fewer authorized roles for a certain resource, the differences between these roles will be relatively large. And this difference will be reflected in the characteristics of user behavior. In clustering analysis, the greater the difference between different roles, the higher the accuracy of clustering (it can be found from the clustering results that the clustering between classes is relatively far and the boundary is obvious).It makes the division between pre-authorized users and pre-privileged users more accurate, so the generated control rules have higher accuracy.
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