Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining

Bansal, Arpit; Sharma, Mayur; Goel, Shalini

doi:10.5120/ijca2017912719

Cited by 69 publications

(50 citation statements)

References 2 publications

(1 reference statement)

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“…The Fuzzy Luhn's Gibbs LDA feature extraction method uses lower cut-off and without lower cut-off for the selection feature method, while Fuzzy Gibbs LDA did not use Luhn's concept. The evaluation results in Table 1 shows that the feature extraction with lower cut-off using equation (1) gives the evaluation value not much different than without the lower cut-off. The difference of metric measurement values between the two methods is very small with the range from 0.0036 to 0.0091.…”

Section: Results and Analysis 31 Fuzzy Luhn's Gibbs Ldamentioning

confidence: 99%

“…Clustering is one of the tasks in data mining to analyze large amounts of data and is able to generate hidden information that is very useful for decision making. Clustering is an unsupervised classification technique that grouping data with similarities into a cluster [1]. The techniques in clustering include partitioning, hierarchical, grid-based, and density-based method [2].…”

Section: Introductionmentioning

confidence: 99%

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Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Prihatini¹,

Putra²,

Giriantari³

et al. 2019

IJECE

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Agglomerative hierarchical is a bottom up clustering method, where the distances between documents can be retrieved by extracting feature values using a topic-based latent dirichlet allocation method. To reduce the number of features, term selection can be done using Luhn's Idea. Those methods can be used to build the better clusters for document. But, there is less research discusses it. Therefore, in this research, the term weighting calculation uses Luhn's Idea to select the terms by defining upper and lower cut-off, and then extracts the feature of terms using gibbs sampling latent dirichlet allocation combined with term frequency and fuzzy Sugeno method.The feature values used to be the distance between documents, and clustered with single, complete and average link algorithm. The evaluations show the feature extraction with and without lower cut-off have less difference. But, the topic determination of each term based on term frequency and fuzzy Sugeno method is better than Tsukamoto method in finding more relevant documents. The used of lower cut-off and fuzzy Sugeno gibbs latent dirichlet allocation for complete agglomerative hierarchical clustering have consistent metric values. This clustering method suggested as a better method in clustering documents that is more relevant to its gold standard.

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Section: Results and Analysis 31 Fuzzy Luhn's Gibbs Ldamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Prihatini¹,

Putra²,

Giriantari³

et al. 2019

IJECE

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show abstract

“…This is done, by using K-Means clustering algorithm. Data clustering is an unsupervised classification method used in creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are Manuscript quite distinct [7], [8]. Another aim of this paper is to predict time, volume and or likelihood of future occurrence of index and non-index crimes for 2018-2022 using ARIMA (1, 0, 7) model.…”

Section: Introductionmentioning

confidence: 99%

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Delima¹

2019

IJMLC

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An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools and methods in analyzing, classifying and predicting crimes. This paper clusters municipalities in Surigao del Norte using K-Means algorithm. This is instrumental in finding identical traits, patterns and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017. Prediction of its occurrence for the year 2018-2022 was also provided using ARIMA (1,0,7) model. Results showed that Surigao City has the most number of recorded index and non-index crimes. Followed by the municipality of Placer, Claver, and Dapa of cluster 2 and Del Carmen of cluster 3. Further, physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%, 25%, 25%, 24%, 24%, and 23% forecasted increase for the year 2018-2022 with the highest occurrence in years 2018, 2018, 2020, 2018, 2020, and 2020 respectively. Future researchers may utilize other data mining techniques supported by a better accuracy result.

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“…in 2017 [10] proposed a system based on k-mean algorithm to deal with two of k-mean problem which is accuracy level and execution time, the proposed system enhance k-mean algorithm and enhanced Euclidian measure, the enhanced Euclidian distance based on find normal distance by using normalization and enhanced k-mean cluster database based on majority voting, the proposed system has double accuracy level then traditional and with very little time.…”

Section: Relation Workmentioning

confidence: 99%

Proposed KDBSCAN Algorithm for Clustering

Hussein

Jalil

2018

ijs

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Science, technology and many other fields are use clustering algorithm widely for many applications, this paper presents a new hybrid algorithm called KDBSCAN that work on improving k-mean algorithm and solve two of its problems, the first problem is number of cluster, when it`s must be entered by user, this problem solved by using DBSCAN algorithm for estimating number of cluster, and the second problem is randomly initial centroid problem that has been dealt with by choosing the centroid in steady method and removing randomly choosing for a better results, this work used DUC 2002 dataset to obtain the results of KDBSCAN algorithm, it`s work in many application fields such as electronics libraries, biology and marketing, the KDBSCAN algorithm that described in this paper has better results than traditional K-mean and DBSCAN algorithms in many aspects, its preform stable result with lower entropy.

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Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining

Cited by 69 publications

References 2 publications

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation

Applying Data Mining Techniques in Predicting Index and non-Index Crimes

Proposed KDBSCAN Algorithm for Clustering

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