Enhancement of Supermarket Business and Market Plan by Using Hierarchical Clustering and Association Mining Technique

2018 ◽  
Author(s):  
Bhagawan Rokaha ◽  
Dhan Prasad Ghale ◽  
Bishnu Prasad Gautam
Keyword(s):  
Hierarchical Clustering ◽  
Association Mining ◽  
Mining Technique ◽  
Market Plan

scholarly journals Comparative Study of Document Clustering Algorithms

2018 ◽  
Vol 7 (4.11) ◽  
pp. 246
Author(s):  
N. M. Ariff ◽  
M. A. A. Bakar ◽  
M. I. Rahmad
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Text Clustering ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Meaningful Result ◽  
Different Types

Text clustering is a data mining technique that is becoming more important in present studies. Document clustering makes use of text clustering to divide documents according to the various topics. The choice of words in document clustering is important to ensure that the document can be classified correctly. Three different methods of clustering which are hierarchical clustering, k-means and k-medoids are used and compared in this study in order to identify the best method which produce the best result in document clustering. The three methods are applied on 60 sports articles involving four different types of sports. The k-medoids clustering produced the worst result while k-means clustering is found to be more sensitive towards general words. Therefore, the method of hierarchical clustering is deemed more stable to produce a meaningful result in document clustering analysis. 

scholarly journals Pengembangan Sistem Informasi Data KB dan Analisis Pola Pemilihan Metode Kontrasepsi Menggunakan Algoritma Sql-Based Fp-Growth

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Yumina Jumiati ◽  
Nurdin Bahtiar
Keyword(s):  
Information System ◽  
Family Planning ◽  
Service Management ◽  
Association Mining ◽  
Number Of Children ◽  
Mining Technique ◽  
Family Planning Programs ◽  
And Control ◽  
Confidence Value ◽  
Research Analysis

<span><em>One of Unit Pelaksana Teknis Pemberdayaan Masyarakat, Perempuan, dan Keluarga Berencana (UPT </em><span><em>BAPERMASPER dan KB) Region XV District of Mijen’s duty is to lead, plan, do, evaluate, and report the </em><span><em>data management in service, management, and control of family planning programs and the </em><span><em>empowerment of women in the district. The inadequate number offield officers (PLKB) often become </em><span><em>spectacle in collecting and reporting the data KB. Family Planning Data Information System (SIDAK) is </em><span><em>developed in order to support the mentioned task to make it easier to report, store, and manage the data. </em><span><em>Additionally, SIDAK is equipped with a data analysis feature with association mining technique using </em><span><em>SQL-Based FP-Growth algorithm. This algorithm analyze the data KB and create a relation (association </em><span><em>rule) between the attributes used for analysis; the wive’s age, the highest education achieved by the </em><span><em>couple, the number of children, the welfare level, and the contraception method. The research analysis of </em><span><em>302 dataKBs resulted in maximum support value of 1.66%, maximum confidence value of 100%, and </em><span><em>maximum lift ratio of 60.24.</em></span></span></span></span></span></span></span></span></span></span></span><br /></span>

scholarly journals Pengembangan Sistem Informasi Data KB dan Analisis Pola Pemilihan Metode Kontrasepsi Menggunakan Algoritma Sql-Based Fp-Growth

2016 ◽  
Vol 15 (1) ◽  
Author(s):  
Yumina Jumiati ◽  
Nurdin Bahtiar
Keyword(s):  
Family Planning ◽  
Association Rule ◽  
Service Management ◽  
Association Mining ◽  
Number Of Children ◽  
Mining Technique ◽  
Family Planning Programs ◽  
And Control ◽  
Confidence Value ◽  
Research Analysis

One of Unit Pelaksana Teknis Pemberdayaan Masyarakat, Perempuan, dan Keluarga Berencana (UPT BAPERMASPER dan KB) Region XV District of Mijen’s duty is to lead, plan, do, evaluate, and report the data management in service, management, and control of family planning programs and the empowerment of women in the district. The inadequate number offield officers (PLKB) often become spectacle in collecting and reporting the data KB. Family Planning Data Information System (SIDAK) is developed in order to support the mentioned task to make it easier to report, store, and manage the data. Additionally, SIDAK is equipped with a data analysis feature with association mining technique using SQL-Based FP-Growth algorithm. This algorithm analyze the data KB and create a relation (association rule) between the attributes used for analysis; the wive’s age, the highest education achieved by the couple, the number of children, the welfare level, and the contraception method. The research analysis of 302 dataKBs resulted in maximum support value of 1.66%, maximum confidence value of 100%, and maximum lift ratio of 60.24.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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