APRIORI ALGORITHM FOR IMPLEMENTATION OF RAW MATERIAL PURCHASE DATA ANALYSIS IN PT MAHAKAM BETA FARMA

PT Mahakam Beta Farma is a manufacturing company in the pharmaceutical field and there are obstacles in the storage of raw materials so that the process of entering and exiting raw materials is not effective. The solution to this problem is to rearrange the location of raw materials in the warehouse to facilitate the distribution of raw materials when entering or leaving so that when needed for the production process does not require much time in the search, which will also have an impact on the smooth production process. The basis for determining the layout of raw materials in the warehouse is to analyze what raw materials are often purchased at the same time for 1 year with data mining using apriori algorithm method. The application used to process the purchase of raw material that is large enough is Tanagra 1.4. The results of this study obtained 12 patterns of purchase of raw materials with a minimum value of 80% support, 90% minimum confidence, and lift ratio o as materials to recommend the re-layout of raw materials in the warehouse

{MDL4BMF}: Minimum Description Length for Boolean Matrix Factorization

Matrix factorizations—where a given data matrix is approximated by a prod- uct of two or more factor matrices—are powerful data mining tools. Among other tasks, matrix factorizations are often used to separate global structure from noise. This, however, requires solving the ‘model order selection problem’ of determining where fine-grained structure stops, and noise starts, i.e., what is the proper size of the factor matrices. Boolean matrix factorization (BMF)—where data, factors, and matrix product are Boolean—has received increased attention from the data mining community in recent years. The technique has desirable properties, such as high interpretability and natural sparsity. However, so far no method for selecting the correct model order for BMF has been available. In this paper we propose to use the Minimum Description Length (MDL) principle for this task. Besides solving the problem, this well-founded approach has numerous benefits, e.g., it is automatic, does not require a likelihood function, is fast, and, as experiments show, is highly accurate. We formulate the description length function for BMF in general—making it applicable for any BMF algorithm. We discuss how to construct an appropriate encoding, starting from a simple and intuitive approach, we arrive at a highly efficient data-to-model based encoding for BMF. We extend an existing algorithm for BMF to use MDL to identify the best Boolean matrix factorization, analyze the complexity of the problem, and perform an extensive experimental evaluation to study its behavior

Frequent Pattern mining with closeness Considerations: Current State of the art

Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It2019;s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future

A New Extraction Optimization Approach to Frequent 2 Item sets

International Journal on Computational Science Applications (IJCSA) ISSN : 2200 – 0011 https://wireilla.com/ijcsa/index.html Current Issue Article Title: A New Extraction Optimization Approach to Frequent 2 Item sets Abstract In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1- itemets supports (cardinal 1 sets), then we prune the 1-itemsets not frequent and keep only those that are frequent (ie those with the item sets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAXMINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of sideby-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets. Keywords Optimization, Frequent Itemsets, Association Rules, Low-Correlation Data, Supports For More Details: https://wireilla.com/papers/ijcsa/V9N2/9219ijcsa01.pd