Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey

Nirmal, Khyati R.; Satyanarayana, K.V.V.

doi:10.11591/ijece.v6i6.pp3047-3051

Cited by 3 publications

(3 citation statements)

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“…K-means Clustering plans to divide n objects into k groups inside which each object has a place with the group with the nearest mean [13]. This method delivers explicitly k entirely unexpected group of most prominent data of qualification.…”

Section: K-means Clusteringmentioning

confidence: 99%

Rank Prediction for Articles and Conference Papers using Machine Learning Techniques.

Subhashini*,

Vijaya,

Bhargav

et al. 2019

IJRTE

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Searching for an optimal article which was given highest and best priority is quite harder based on requirements. Ranking is one of the best measure or a method to get the best rated and optimal article or a conference or a research paper through this huge Internet World. As Technology been increasing day by day Artificial Intelligence is the first step to get through any problem for a solution Machine learning is also an important aspect of Artificial Intelligence. Machine Learning is best known for classifying, categorizing and predicting. Rank prediction can be done through many different algorithm implementations in machine learning. But choosing the best is important for accurate results. This paper gives the most accurate results of algorithms that can be used for rank predictions for articles. To simplify and resolve this problem, solutions were given in many different ways but to achieve accuracy is necessary, in previous models this is given using supervised learning only. We proposed this research work with perfect results using both supervised and unsupervised learning. Neural Networks is the best algorithm in supervised learning for classifying and predicting within data. In unsupervised learning we used K-means clustering because of grouping the data. This work helps the user(s) for optimal search of an article and also gives a competitive spirit for author to get into the top, totally this is implemented using Machine Learning Techniques of Neural Networks, K-Means Algorithm which is a mixture of supervised and unsupervised learning for predicting ranks.

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Section: K-means Clusteringmentioning

confidence: 99%

Rank Prediction for Articles and Conference Papers using Machine Learning Techniques.

Subhashini*,

Vijaya,

Bhargav

et al. 2019

IJRTE

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“…It is used to analyze psychology of humans and their relationships [3]. Clustering [4]. Some of the data sets contains any type of data such as numeric or categorical or both and differs from their attributes.…”

Section: Introductionmentioning

confidence: 99%

Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

Rani¹,

Sucharita²,

Satyanarayana³

2018

IJECE

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Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.

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“…In [5], automatic initial number of center k was proposed for K-Means to form clusters on big data by using map reduce paradigm. In [6], one of cluster quality measurement methods (i.e., index silhouette) was used to measure the quality of document group which have financial risks. Silhouette index is used because of its simplicity to measure how well a document is placed in a cluster.…”

mentioning

confidence: 99%

Optimum partition in flight route anomaly detection

Pusadan

Buliali

Ginardi

2019

IJEECS

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Anomaly detection of flight route can be analyzed with the availability of flight data set. Automatic Dependent Surveillance (ADS-B) is the data set used. The parameters used are timestamp, latitude, longitude, and speed. The purpose of the research is to determine the optimum area for anomaly detection through real time approach. The methods used are: a) clustering and cluster validity analysis; and b) False Identification Rate (FIR). The results archieved are four steps, i.e: a) Build segments based on waypoints; b) Partition area based on 3-Dimension features P1 and P2; c) grouping; and d) Measurement of cluster validity. The optimum partition is generated by calculating the minimum percentage of FIR. The results achieved are: i) there are five partitions, i.e: (n/2, n/3, n/4, n/5) and ii) optimal partition of each 3D, that is: for P1 was five partitions and the P2 feature was four partitions

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Issues of K Means Clustering While Migrating to Map Reduce Paradigm with Big Data: A Survey

Cited by 3 publications

References 0 publications

Rank Prediction for Articles and Conference Papers using Machine Learning Techniques.

Rank Prediction for Articles and Conference Papers using Machine Learning Techniques.

Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey

Optimum partition in flight route anomaly detection

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