Beyond independence: probabilistic models for query approximation on binary transaction data

D. Pavlov; H. Mannila; P. Smyth

doi:10.1109/tkde.2003.1245281

Beyond independence: probabilistic models for query approximation on binary transaction data

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2003.1245281 ◽

2003 ◽

Vol 15 (6) ◽

pp. 1409-1421 ◽

Cited By ~ 31

Author(s):

D. Pavlov ◽

H. Mannila ◽

P. Smyth

Keyword(s):

Probabilistic Models ◽

Transaction Data ◽

Query Approximation

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Management of Economics and State "Bottom"

Issues of Risk Analysis ◽

10.32686/1812-5220-2020-17-6-76-91 ◽

2020 ◽

Vol 17 (6) ◽

pp. 76-91

Author(s):

E. D. Solozhentsev

Keyword(s):

Quality Of Life ◽

Artificial Intelligence ◽

Probabilistic Models ◽

Human Life ◽

Special Software ◽

Human Quality ◽

Relationship Of ◽

The Relationship

The scientific problem of economics “Managing the quality of human life” is formulated on the basis of artificial intelligence, algebra of logic and logical-probabilistic calculus. Managing the quality of human life is represented by managing the processes of his treatment, training and decision making. Events in these processes and the corresponding logical variables relate to the behavior of a person, other persons and infrastructure. The processes of the quality of human life are modeled, analyzed and managed with the participation of the person himself. Scenarios and structural, logical and probabilistic models of managing the quality of human life are given. Special software for quality management is described. The relationship of human quality of life and the digital economy is examined. We consider the role of public opinion in the management of the “bottom” based on the synthesis of many studies on the management of the economics and the state. The bottom management is also feedback from the top management.

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Pemanfaatan Metode K-Means Dalam Penentuan Persediaan Barang

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic ◽

10.33558/piksel.v6i1.1398 ◽

2018 ◽

Vol 6 (1) ◽

pp. 41-48

Author(s):

Santoso Setiawan

Keyword(s):

Data Mining ◽

Stock Management ◽

Transaction Data ◽

Business People ◽

Cluster Data ◽

Data Clusters ◽

Inventory Strategy ◽

Transactional Data

Abstract Inaccurate stock management will lead to high and uneconomical storage costs, as there may be a void or surplus of certain products. This will certainly be very dangerous for all business people. The K-Means method is one of the techniques that can be used to assist in designing an effective inventory strategy by utilizing the sales transaction data that is already available in the company. The K-Means algorithm will group the products sold into several large transactional data clusters, so it is expected to help entrepreneurs in designing stock inventory strategies. Keywords: inventory, k-means, product transaction data, rapidminer, data mining Abstrak Manajemen stok yang tidak akurat akan menyebabkan biaya penyimpanan yang tinggi dan tidak ekonomis, karena kemungkinan terjadinya kekosongan atau kelebihan produk tertentu. Hal ini sangat berbahaya bagi para pelaku bisnis. Metode K-Means adalah salah satu teknik yang dapat digunakan untuk membantu dalam merancang strategi persediaan yang efektif dengan memanfaatkan data transaksi penjualan yang telah tersedia di perusahaan. Algoritma K-Means akan mengelompokkan produk yang dijual ke beberapa cluster data transaksi yang umumnya besar, sehingga diharapkan dapat membantu pengusaha dalam merancang strategi persediaan stok. Kata kunci: data transaksi produk, k-means, persediaan, rapidminer, data mining.

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