Ijraset Journal For Research in Applied Science and Engineering Technology
Authors: S. Sivasankari, Dr. S. Sukumaran
DOI Link: https://doi.org/10.22214/ijraset.2024.64224
Certificate: View Certificate
Data mining is the area of computational science consider as the subset of Artificial Intelligence, Machine Learning that concentrate on cognizing patterns and structures in data to promote learning, analyzing, and decision making beyond human interaction. In simplest term, data mining allows the user to feed data mining algorithms on an immense amount of data and then make the computer to make datadriven recommendations and decisions based on the input or training data. Data is the essential component of all business. Data mining with supervised and unsupervised methods is the key to unlock the value of business data and keep ahead of the competition. This paper presents a comprehensive survey on Data mining process, supervised, unsupervised methods, through a survey of literature considering the significant research areas using classification, clustering, and association rule mining.
I. INTRODUCTION
Data mining is the procedure to find patterns, [7] anomalies, and correlations in a large data sets in order to predict outcomes. With the use of broad range of techniques, the process of digging knowledge from data is done to discover hidden connections and predict future trends. Hence, it is referred as "Knowledge Discovery in Databases (KDD)”. The advances in processing power and speed transform the nature of analyzing to move beyond manual, tedious and timeconsuming practices to quick, easy and automated data analysis. [36] The increasing complexity of the data leads to higher potential to uncover relevant insights. Data mining is at the heart of analysis across a variety of industries and disciplines. . The field comprises of three scientific disciplines namely Statistics, Artificial Intelligence, and Machine Learning.
Modernday companies cannot live without data. [13] To stay ahead of the competition with smart decisions, they have to evolve and keep up with technological evolution and upcoming digital trends. Therefore, businesses today are prioritizing staying ahead of all the new developments in the field of data science and analytics with data mining techniques. Some most important data mining applications include Financial Analysis, Telecommunication Industry, Intrusion Detection, Retail Industry, Higher Education, Energy Industry, Spatial Data Mining, Biological Data Analysis, Scientific Applications, Manufacturing Engineering, Criminal Investigation, Counter terrorism etc.
A. Data Mining Process, Methods
Data mining (KDD) process comprise of Data selection, Preprocessing, Transformation, Mining, Evaluation and Knowledge gaining while the CRISPDM (CrossIndustry Standard Process for Data Mining) process comprise of Business understanding, data understanding, Data preparation, Modeling, evaluation and Deployment.
The mining methods are classified into three types [15] namely supervised with labeled data, unsupervised without label, and semisupervised that has partially labeled with more unlabeled data.
II. RELATED WORKS WITH VARIANT DATA MINING METHODS
The recent research papers that implemented data mining classification, clustering, association rule mining methods for variant applications with its process, limitations is discussed in this section elaborately.
A. Supervised Learning methods – Classification, Regression
Anjuman Prabhat et al [2] compared two machine learning classification algorithms naive bayes and logistic regression for sentiment classification on movie reviews. TFIDF method with term/word level sentiment classification was carried out in MapReduce and Hadoop Distributed File system for categorizing twitter movie reviews with two polarities positive and negative. The resultant values of these two algorithms showed that logistic regression has better accuracy level with 76.76 % and naive bayes has 66.66 % for 6 MB dataset. However, the accuracy is inadequate.
Sanditya Hardaya et al [30] designed a classification model Support Vector Machine (SVM) for the classification of the complaints and proposals using text mining technique. In preprocessing, stemming, case folding, filtering, synonym recognition and the word vector was created using Term Fequency (TF), Inverse document frequency (IDF) and Singular Value decomposition (SVD). The performance was evaluated and found that classification model with stemming, synonym recognition was the most accurate among others with 91.37% accuracy rate. Even though the method TFIDF can be tried to improve the accuracy.
Ayesha Anzer et al [5] implemented a linear regression approach to predict final exam scores from early course assessments of the students. The datasheet comprise of different attributes like gender, quiz 1, quiz 2, quiz 3, quiz 4, assignment 1, assignment 2, assignment 3, midterm, and final exam grades. In preprocessing, unneeded attributes are removed and missing values were modified then linear regression model was applied. Finally, it was noted quizzes and total assignment score was most important factor to predict the academic performance from the values tstatistics with 3.34 for Quiz 2 and pvalue with 0.64 for Quiz 1.
Chittem Leela Krishna et al [6] applied Deep Neural Net MLP (feed forward) classifier for patient analysis of Cleveland Heart Disease data from UCI. Initially, data preprocessing is done with inter quartile range, missing value is replaced, best first search method was used to have subset of features and the five class is transformed to 2 (binary) class. The proposed deep neural network consists of three hidden layers with the size 15, 12, 5, sigmoid activation function, and mean absolute error as a loss function. The Learning and momentum rate was 0.15 and 0.2.
From the results, it was found that the proposed model deep neural network obtained 85.4 % accuracy and it is followed by support vector machine 84.4 %, naive bayes with 83.8 %, decision tree with 80.8 %.
Najat Ali et al [19] put forth a combination of similarity measure in Knearest neighbor to investigate the performance on heterogeneous datasets The different domains are Hypothyroid, Hepatitis, Treatment, Labour training evaluation, Catsup, Azpro.
For the analysis, Euclidean, Manhattan, combined measures namely EuclideanJaccard, CosineJaccard, CanberraJaccard was used in R. In preprocessing, irrelevant features are removed, missing values were replaced and numerical features were normalized. The training and testing set consists of 80%, 20 %. The overall results proved that the combination of discussed measures gave a promising result with the K value 1. Among all the datasets, the discussed combined measures gave 100 percent accuracy for hypothyroid dataset. Still, the work does not prove the accuracy with minimized processing time.
Farhan Ullah et al [10] proposed a Deep feed forward Neural Collaborative Filtering for educational services recommendation. The experiments was performed in keras on the benchmark good books 10k. Initially, the users' identifier and books identifier features are mapped into Ndimensional vectors and then this array is feed into multilayer perceptron. Variant activation functions tanh, Elu, sigmoid was compared with the proposed function Relu. Four layerhidden architecture is employed with the neurons (32, 64, 128, and 256). From the results it was observed that the model with 4 hidden layers, L2 regularization, Huber loss function, Relu activation function, Adam optimizer gave less RMSE0.82) and Mean absolute error (MAE0.60) and outperforms the existing method. Nevertheless, processing time for each hidden layer structure with the activation function must be noted.
Lalit Mohan et al [17] implemented support vector machine (SVM) with variant kernels radial basis kernel, linear kernel, polynomial kernel, sigmoid kernel on iris dataset to assess the performance. From the initial results it was observed, SVM with RBF kernel gave high accuracy with 97.3 % and it followed by linear kernel (96.66%), polynomial (95.33%) and sigmoid (88.66%). Then, the parameters in the kernel selection cost, epsilon was modified in the RBF kernel to have further improvement in the accuracy and the accuracy was reached from 97.3 % to 98.6%. However, the proposed method takes longer time.
Niko Reunanen et al [20] proposed two ensemble based outlier detection method KNN with LOF and logistic regression with LOF in heterogeneous datasets to handle bias and variance in outlier analysis. The work applied data bagging and feature bagging. In first strategy KNN and LOF were chosen for outlier detection with optimized outlier ensemble (OOE). KNN was applied with the distance functions Manhattan, Euclidean, Chebyshev, Cosine, Correlation, and Canberra. In second strategy, LOF with logistic regression was chosen. The experiment was done with benchmark datasets and found that OOE method found 50 % outliers with best AUC 0.816. Still only one score AUC is used hence other parameters can be used.
Raed Alsini et al [27] put forth a method Isolation Forest based on a Sliding window (SW) with LOF (IFSLOF) to quantify the anomalies and outliers in concrete mixtures. Different sizes of windows 100, 200, 300, 400 were used to evaluate the performance of LOF alone, LOF with sliding window LOFSW and LOF with Isolation forest IFSLOF. From the listed value in the results, it was noted that for 100, 200 size window LOF gave high accuracy 90.89%, 96.68% respectively, for 300 LOFSW gave high accuracy 95.38%and 400 IFSLOF gave high accuracy 97.48%. Hence, the proposed method IFSLOF needs large window size. However, the method IFSLOF takes high processing time.
Wenxian Feng et al [38] designed an improved random forest algorithm FSRF for six types of datasets in UCI. Feature selection methods filter method with embedded method and wrapper method was used. This subset was applied to random forest algorithm. The training and testing set is split into 70:30 and six types of datasets from UCI (LSVT, srbct, Arcene, gistte_train, Parkinson, cleveland, ionosphere) were taken for analysis. From the observed values, it was noted that filter wrapper combined method with random forest gave high accuracy for LSVT 89%, srbct 100%, arcene 97% and ionosphere 95%. The overall results showed the combined feature selection method proved to be the best but, the time complexity of the algorithm is increased.
Archana R. Panhalkar et al [3] introduced Tangent Weighted Decision Tree Forest (TWDForest) C4.5, for heterogeneous dataset. It creates a weighted decision tree forest. Along with that ensemble method, bootstrap sampling was used. The weight of 1.0 was assigned to each attribute. The method was assessed with 15 datasets taken from UCI repository. The work was also evaluated for variant number of trees and noted the proposed ensemble TWDForest with 100 trees produced better accuracy 98.4%. But, the processing time is very long as compared with random forest.
He Xu et al [11] introduced a novel outlier detection algorithm KNNLOF for activity recognition system based on a multisensor. Initially, knearest neighbors algorithm is applied to divide different areas for outlier attributes then, a hierarchical adjacency order is proposed. Hence, the reachable distance of an object was redefined with the average sequence distance in the hierarchy. The work was assessed with different methods LOF –Local outlier factor, RDOS relative desity based outlier, and COFconnectivity based outlier factor with K the values 20, 45. For the value 45, the proposed method KNNLOF found 101 outliers and has high area under curve value 0.96. But still, the time complexity is high.
Xiaoyu Luo et al [39] implemented Support Vector Machines (SVM) model in classifying English text, documents. The samples were collected from the UCI repository.
The work was experimented in WEKA tool for four type of datasets with SVM, NB, and LR. From the listed values. the proposed method SVM gave high precision values 0.88, 0.76, 0.63 for the datasets 1, 2, 3 respectively. But still, the precision can be improved also processing time for each classification method might be noted.
B. Unsupervised Learning methods – Clustering
Pedro albuquerque et al [24] proposed a customer segmentation model for real Mobile TV service through Support Vector Clustering (SVC). SVC based on support vector machine has a kernel trick and this work used inner product kernel. The data were initially standardized to possess zero mean and unit variance. The proposed method was assessed against hierarchical agglomerative with the linkage criteria ward, single, complete, average, mcquitty, meadian, centroid and found that the proposed method SVC has less sum of squares i.e 1.06. Still, the processing time is not listed for all the discussed methods.
Raja Kishor et al [28] presented a hybridization technique of ExpectationMaximization (EM) and Kmeans (KM) methods on benchmark datasets. These two algorithms was made hybrid in the order EM, KM. For the assessment, three datasets from UCI namely Magic Gamma, Poker Hand, Letter Recognition, three synthetic datasets were used. Different K values starts from 10 15 was applied. The minimum processing time 0.111 seconds was taken for magic gamma dataset with K =11. The minimum sum of squares was obtained for the K value 15 as 32.75 for Poker handset dataset. Though, variant distance measures or kernel function with Kmeans can be substituted for further improvement.
Saptarshi Sengupta et al [31] coupled Quantumbehaved Particle Swarm Optimization with the Fuzzy CMeans Clustering algorithm and was tested in Matlab on a suite of datasets such as iris, breast cancer wiscosin, seed dataset, mammographic mass, sonar dataset from the UCI Machine Learning Repository. The proposed method was compared with particle swarm optimization with Kmeans and Quantum particle swarm optimization with Kmeans and from the observed results it was found the proposed method has highest Fmeasure 0.9641 for breast cancer dataset. However, the processing time for each method is not noted.
Ping Yang et al [25] presented an improved method for outlier detection on benchmark datasets to handle time overhead in LOF. This paper applied a Neighbor Entropy Local Outlier Factor (NELOF). Firstly, SelfOrganizing Feature Map (SOFM) algorithm was improved to ease the calculation of each data point’s local outlier factor . Then, Kdistance neighborhood was replaced with relative Kdistance neighborhood to redefine the local outlier factor. SOFM clustering was improved with canopy initialization for neurons. The work was conducted using python on seven different datasets syn_data1 and syn_data2, Iris, Wine, Glass, TEBHR, LDPA from UCI. The improved SOFM gave high cluster quality 0.97 and then the method NELOF gave high accuracy outlier detection 98.7 % with less execution time around 0.8000 sec. Still, the work can be experimented for high dimensional data.
Praveen et al [26] analyzed three linkage criteria on benchmark dataset using Hierarchical agglomerative clustering such as single, complete, average, and average weighted. Dataset was taken from UCI repository and evaluated using JAVA programming language. From the results, it was observed that single linkage takes least time i.e. 3591 ms. However, the details of dendrogram and number of clusters formed is not focused.
Rishabh Ahuja et al [29] built a movie recommender system with KMeans Clustering and KNearest Neighbor algorithm. The movielens dataset was taken from kaggle and implemented in python programming language. In preprocessing step, utility matrix was created. Then Kmeans algorithm by considering within Sum of Squares is implemented for right number of clusters. Then, for prediction for top n movie rating, KNN algorithm was applied with the Pearson coefficient similarity. In final it was noted even for large number of clusters (68) the RMSE value is 1.231. But, in existing the same value was obtained for 19 clusters. However, within Sum of Squares is applied for choosing number of clusters but its value is not noted.
IuliaMaria Radulescu et al [14] put forth a method to cluster short text documents using a modern document embedding model, specifically Doc2Vec with Distributed Memory Model (PVDM), and Distributed Bag of Words Model (PVDBOW) for DBSCAN and HDBSCAN on a set of 12263 articles extracted from the Arxiv database. Stemming, Lemmatization were used for preprocessing. The results showed that PVDM embedding wordnet lemmatizer obtained 0.56 Adjusted RAND index for DBSCAN and 0.59 Adjusted Mutual Information values thus produce qualified cluster. However, number of clusters was not discussed.
Noor S. Sagheer et al [21] used the canopy clustering algorithm with kmeans clustering algorithm for health insurance big dataset. Two types of mode was chosen serial in which Kmeans algorithm is implemented in a single machine and Parallel mode in which the data parallel divides the whole data into small subsets. Results were generated and found that the parallel mode for Kmeans with canopy took lesser time 1.048 hour and Kmeans without canopy took 1.847 hour thus proved the efficiency. But, the work concentrates on speed only, the quality of the cluster is not proved.
Shanshan Li et al [32] put forth an improved DBSCAN based on neighbor similarity that utilizes Cover Tree. Datasets includes PAM4D, HOUSE, REACTION, MOCAP, and FONT from UCI repository for analysis.
This proposed method was compared with two earlier proposed one namely DBSCAN and ρ approximate DBSCAN. Initially all missing values are set to zero, and all data value are normalized. The analysis was carried out for variant e, min points.
From the results, it was proved that proposed method DBSCAN with cover tree speeds up the process by having least time 19.68 seconds for the REACTION dataset with Dimension 28, e 5000 and minpts 50. Nevertheless, number of clusters and the quality of the cluster is not specified.
Joelson Antonio dos Santos et al [16] put forth a method that combine the quality of DBSCAN and hierarchical clustering on benchmark. It combined the aspects of density based clustering and hierarchical clustering that produce a complete densitybased hierarchy clustering using Map reduce framework. Recursive Sampling Approach was created to draw sample from a whole dataset and then it was combined with a data summarization technique called data bubbles with parallelism. The proposed method proved with highest Adjusted Rand Index as 0.881 for all the datasets. Also, regarding runtime the hierarchical category agglomerative took minimum 2.11 min. which was less than the divisive. Memory utilization is not proved.
C. Unsupervised Learning methods  Association rule mining
Ashish Shah et al [4] proposed a modification to the apriori algorithm by using a hash function which divides the frequent item sets into buckets. Further, a novel technique was used in conjunction to eliminate infrequent itemsets from candidate set. This top down approach saves time and space. The proposed technique was explained with example contains 15 transactions, minsupport value 20%. The work was proved with number of scans that was reduced to one to get frequent itemset but it should be evaluated until the rules generated. Also, the processing time must be noted for each scan.
Andi et al [1] implemented FP growth for book search to get the association between each borrowed by the student. The results was analyzed for 1 frequent itemset, 2 frequent tiemset and 3frequent itemset with support count and filter the largest count itemsets separately. With the support count 4, for 13 frequent itemset list, 21 rules were formed. But still, the algorism has to improved to get more number of rules.
Le Hoang Son et al [18] designed a new mining algorithm Association rule mining Apriori based on Animal Migration Optimization (AMO), called ARMAMO, to reduce the number of association rules. For the analysis a supermarket data was taken and assessed with variant support and confidence in MATLAB tool. The results indicated that ARMAMO provides better performance for the support, confidence values starts from 0.1  1.0. ARM –AMO generates rules around 3120514 respectively, memory consumption around 41.2 MB  3.1MB respectively and processing time around 312 sec.  31 sec for the support and confidence values 0.11.0. Still, the work can be experimented with other parameters such as lift, leverage etc.
Dawei Dong et al [8] proposed a Improved Ant lion optimizer (IALO) with FPGrowth (FP) in nursery school dataset. The main idea of IALOFP includes Random walks of ants, Trapping Antlion’s hole, Building trap, Sliding Ants towards Antlion, Catching Prey & Rebuilding the hole and finally Elitism with a good fitness that contains support and confidence. The results showed IALOFP produced 170 rules that was more than genetic algorithm, ALO alone methods. Nevertheless, the processing time for each method is not discussed.
Xu Hongfei et al [40] applied Apriori algorithm with undirected graph that speeds itemset generation instead of matrix to store itemsets and so candidate set is not needed. The proposed method used depth first search algorithm to traverse undirected itemset graph only once to acquire new frequent itemsets. The method was implemented on VC++ language with a sample data, and found that improved one need less processing time starts from 10 sec. to 25 sec. for the support count 0.3 to 0.1. However, the number of association rule is not focused.
HuanBin WANG et al [12] improved the apriori algorithm based on Map Reduce model with the idea of parallelism. First, local frequent itemsets on each sub node in the cluster are calculated and then merged into the global candidate itemsets. Finally, the frequent itemsets that meet the conditions were filtered based on minimum support threshold. Thus improved algorithm needs to scan the transaction database only twice. The execution process is verified by a specific example with 10 transactions but without the evaluation parameters such as number itemset generated, number of rules formed, processing time.
Norulhidayah Isa et al [22] applied FPGrowth to associate the relationship between the supply needed and the projects acquired for stopping maverick buying in electric company. The procedure implemented in Neddy Enterprise Sdn. Bhd (NE) electrical company. Initially, based on the purchase order, date, supply, quantity the data was consolidated and then FPGrowt with support 0.2, confidence 0.8 value the frequent itemsets and rules were formed in Rapidminer. However, number of rules for the prescribed values, processing time was not listed with consolidated dataset.
Table I. summarizes the recent works on supervised learning classification methods Naive bayes, Logistic regression, SVM, Linear regression, Deep learning multi feed forward neural network, KNN, SVM, Isolation forest, Random Forest, Decision tree C4.5 on the applications namely sentiment analysis, Text mining, education, Biological analysis, Outlier analysis, Text classification and some are implemented on heterogeneous data.
TABLE I. Summary of Related works on Supervised Learning method Classification
Author, Year 
Method 
Application 
Merits 
Demerits 
Anjuman Prabhat et al 2017 [2] 
Word vector: TFIDF Classifier: Naive bayes, Logistic regression 
Sentiment analysis 
Categorize twitter movie reviews with two polarities positive, negative. Logistic regression got high accuracy with 76.76 % 
Analysis based on processing time is not carried out. 
Sanditya Hardaya et al 2017 [30] 
Preprocessing: Stemming, Case folding, synonym recognition. Text mining Method: TF, IDF, SVD Classifier: SVM model 
Text Mining 
Precision reached to higher level 91.37% on community complaints and proposals dataset. 
SpatialTemporal details should be included to have indepth analysis. method TFIDF has more advantage but it is not used.

Ayesha Anzer et al 2018 [5] 
Preprocessing: Removing irrelevant attributes, Replacing missing values. Classifier: Linear regression 
Education 
Statistical analysis was done to get the most important attribute with ptest value, Ttest value using linear model. 
Classification is not done to assess the students’ performance. 
ChittemLeela Krishna et al 2019 [6] 
Preprocessing: Missing value replacement , Inter quarile range, Feature subset with best first search. Classifier: Deep multi feed forward network. 
Biological analysis 
The accuracy was 85.4% than other methods for Cleveland Heart Disease data. 
Still, have to improve the accuracy by adjusting the parameters in the deep neural classifier. 
Najat Ali et al 2019 [19] 
Training  80:20 Preprocess: Removal of Irrelevant features, replacing Missing values , Normalization. ClassifierKNN with combined similarity measure 
Heterogeneous applications 
Accuracy is high 100% for Hypothyroid dataset. 
Accuracy is less for other applications.

Farhan Ullah et al 2020 [10] 
Collaborative Filter. Classifier: Deep multi feed forward Neural network 
Education Recommendation system 
4 hidden layers, L2 regularization, Huber loss function, Relu activation function, Adam optimizer was utilized and obtained RMSE 0.82 on book dataset. 
RMSE value ≥0.5 reflects the poor ability of the model. 
Lalit Mohan et al 2020 [17] 
Cross Validation Classifier: SVM with RBF Kernel 
Biological analysis 
High accuracy 98.6 % was obtained on iris dataset. 
Takes high processing time. 
Niko Reunanen et al 2020 [20] 
Outlier Factor: LOF Classifier: KNN with optimized ensemble 
Outlier analysis 
Handle bias and variance. AUC 0.816 on benchmark datasets 
Finds 50 % outliers only. 
Raed Alsini et al[ 2020 [27] 
Outlier Factor: LOF Classifier : Isolation Forest with sliding window 
Outlier analysis 
Outliers in concrete mixture dataset is found with varying window size and highest accuracy was 97.48 % for 400 size window. 
Takes high processing time.

Wenxian Feng et al 2020 [38] 
Training:70:30 Preprocessing: Filterwrapper combined. Classifier: Random Forest 
Heterogeneous applications 
Combined feature selection method with the classifier proved to be the best with 100 % accuracy on benchmark dataset. 
Time complexity of the algorithm is increased. 
Archana R.Panhalkar et al 2021[3] 
Classifier: Tangent Weighted Decision Tree Forest C4.5 
Heterogeneous applications 
Produced better accuracy 98.4 % on benchmark dataset 
The processing time is very long. 
He Xu et al 2021[11] 
Outlier factor : LOF Classifier : KNN 
Outlier analysis 
High number of outlier 101 for the K value 45 on activity recognition system. 
Neighbor search algorithms is not concentrated. 
Xiaoyu Luo et al 2021[39] 
Classifier: SVM 
Text Classification 
High precision value 0.88 was obtained for English text document. 
Text preprocessing methods is not implemented. 
Table II, summarizes the unsupervised learning clustering methods such as Support vector clustering, Expectation Maximization, KMeans, Hierarchical agglomerative, Fuzzy CMeans, Self Organizing Map, Density based DBSCAN and Canopy clustering on the applications namely customer segmentation, movie recommendation, text clustering, Health insurance and some are applied on heterogeneous benchmark datasets with its merits and demerits.
TABLE II. Summary of Related works on Unsupervised Learning method Clustering
Author, Year 
Algorithm 
Application 
Merits 
Demerits 
Pedro albuquerque et al 2015 [24]

Preprocessing: Standardize. Clustering: Kernel based Support vector clustering with inner product kernel 
Customer segmentation 
Cluster distribution with 5 clusters is proved with less sum of squares 1.06 on Mobile TV service data. 
Variant size cluster is not tried to get further least sum of squares. 
Raja Kishor et al 2016 [28] 
Partition KMeans with Probability based EM clustering.

Heterogeneous applications 
Variant K value was tried.. Minimum processing time 0.11 sec. and least sum of squares 32.75 was obtained for K value 15. 
KMeans with Euclidean cannot handle outliers. 
Saptarshi Sengupta et al 2018 [31] 
Quantumbehaved Particle Swarm Optimization with Partition based Fuzzy CMeans clustering 
Heterogeneous applications 
FCM helps to partition data based on membership probabilities. Breast cancer application has highest Fmeasure 0.9641. 
Randomly chosen centroids leads to more iteration. 
Ping Yang et al 2019 [25] 
Neural network based SOM clustering with LOF 
Heterogeneous applications 
SOM, Relative Kdistance redefines the LOF. Cluster quality 0.97, outlier detection 98.7 %, less time 0.8000 sec on benchmark datasets was obtained. 
The work is not proved for high dimension datasets. 
Praveen et al 2019 [26] 
Linkage based Hierarchical Agglomerative clustering

Heterogeneous applications 
Analyzed all the linkage criteria single, complete, average, average weighted on benchmark dataset. Single linkage takes least time 3591 ms 
Details of dendrogram and number of clusters formed is not focused.

Rishabh Ahuja et al 2019 [29] 
Partition clustering KMeans with supervised classification KNN 
Movie recommendation system 
Utility matrix with user and movie rating was created. Top rated Movies was listed. Large number of clusters (68) with RMSE value 1.231 was obtained on movie review dataset 
Processing time for producing the large number of clusters is not noted. 
IuliaMaria Radulescu et al 2020 [14] 
Density based clustering DBSCAN 
Text clustering 
Cluster short text documents on Arxiv database using Distributed Bag of Words. Qualified clusters was generated with 0.56 ARI and 0.59 Adjusted Mutual Information. 
Number of clusters with data distribution is not explained. Time to create bag of words is not listed. 
Noor S. Sagheer et al 2020 [21] 
Partition clustering KMeans with Canopy preclustering

Big data Health insurance 
The work applied serial, Parallel modes. Parallel mode for Kmeans with canopy took less time 1.048 hour on insurance data. 
The cluster quality is not proved.

Shanshan Li et al 2020 [32] 
Preprocessing: Replacement of missing values, Normalization. Density based DBSCAN with cover tree.

Heterogeneous applications

Cover tree data structure ease the process in finding neighbor similarity. Speeds up the process by having least time 19.68 seconds on benchmark datasets. 
Number of clusters and the quality of the cluster is not specified.

Joelson Antonio dos Santos et al 2021[16] 
Recursive Sampling with Density based DBSCAN, hierarchical Agglomerative. 
Heterogeneous applications. 
Sampling technique with data bubbles ease the analysis. Highest ARI 0.881 on benchmark datasets. 
Memory utilization, processing time is not proved. 
Table III, summarizes the unsupervised association rule mining with Apriori and FPGrowth algorithms with the applications supermarket, education, library, supply chain in company and some are implemented in sample transaction dataset.
TABLE III. . Summary of Related works on Unsupervised Learning method Association rule mining
Author,Year 
Algorithm 
Application 
Merits 
Demerits 
Ashish Shah et al 2016 [4] 
Improved Apriori, Hash function 
Sample Transaction 
Saves time and space with hash function. Number of scans to generate frequent itemset is reduced. 
No. of rules, time to generate itemsets with, without hash function , is not given. 
Andi et al 2018 [1] 
FPGrowth 
Library 
Analyzed 1 frequent itemset, 2 frequent itemset and 3frequent itemset. Generate 21 rules with confidence 50%. 
Variant support, confidence values is not tried to get more number of itemsets and rules. 
Le Hoang Son et al 2018 [18] 
Apriori with Animal Migration Optimization 
Supermarket 
Produced compressed number of rules. Assessed with variant support and confidence values. 3120 rules were generated within 312 sec., 41.2 MB memory for the support, confidence values 0.1, 01 respectively. 
Other than support, confidence values lift, leverage is not included to have compact rules. 
Dawei Dong et al 2019 [8] 
FPGrowth with Improved Ant Lion optimization. 
Education 
Generate 170 rules. 
Variant combination of support, confidence values is not tried. 
Xu Hongfei et al 2019 [40] 
Improved Apriori with undirected graph. 
Sample Transaction 
Instead of candidate set generation, undirected graph was used that reduce number of scans in generating frequent itemsets. 
Association rule generation is not focused.

HuanBin WANG et al 2021[12] 
Apriori with Parallelism. 
Sample Transaction 
Parallelism method leads to scan the transaction database only twice. 
The work ends with frequent itemset but doesn’t produce association rules. 
Norulhidayah Isa et al 2021[22] 
FPGrowth 
Supply chain in company 
With support 0.2, confidence 0.8 the frequent itemsets and rules were formed to get the pattern of purchasing. 
Number of rules for the prescribed values is not specified. 
Data mining categories supervised, unsupervised with its incorporated algorithms and preprocessing, text processing, procedures is very beneficial to have interesting and useful patterns in data. This survey discusses about recent works in variant applications with data mining methods classification, clustering, association rule mining elaborately to have indepth knowledge. It is observed in supervised learning, decision tree based algorithms Isolation forest, Random forest and C4.5 gave better results for the applications such as outlier analysis and for heterogeneous applications. SVM is better suits for text oriented applications. KNN is proved to be the best for biological analysis, outlier detection. Deep learning multilayer feed forward network applied on education analysis obtained accurate pattern. The Unsupervised clustering category has different performance evaluation measures based on its kind. The hybrid technique Partition based algorithm KMeans with probability based Expectation maximization on heterogeneous applications gave better result as it has less sum of squares and processing time and Fuzzy CMeans of the same category with PSO gave better FMeasure on heterogeneous applications. For customer segmentation, kernel based SVC provides least sum of squares thus proved cluster quality. The hybrid method Density based DBSCAN with hierarchical proved its efficiency on heterogeneous dataset with high adjusted RAND index. For outlier detection, neural network based self organizing map with LOF factor produced good results with high cluster quality and outlier detection accuracy. The performance of unsupervised learning association rule mining states that combination of Apriori with Animal Migration Optimization proved to be the best for supermarket data, while considering number of scanning and processing time, Apriori with hashing, undirected graph, with parallelism gave better results, regarding FPGrowth, the algorithm with Improved Ant Lion optimization produced more number of rules. From the overall analysis it reveals, that all categories of algorithms if processed with suitable preprocessing and combined methods it gives efficient results on any type of applications.
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Copyright © 2024 S. Sivasankari, Dr. S. Sukumaran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET64224
Publish Date : 20240912
ISSN : 23219653
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