An Efficient Technique for mining Association rules using Enhanced Apriori Algorithm A Literature survey

At present Data mining has a lot of e-Commerce applications. The key problem in this is how to find useful hidden patterns for better business applications in the retail sector. For the solution of those problems, The Apriori algorithm is the most popular data mining approach for finding frequent item sets from a transaction dataset and derives association rules. Association Rules are the discovered knowledge from the data base. Finding frequent item set (item sets with frequency larger than or equal to a user specified minimum support) is not trivial because of its combinatorial explosion. Once item sets are obtained, it is straightforward approach to generate association rules with confidence value larger than or equal to a user specified minimum confidence value. Apriori uses bottom up strategy. It is the most famous and classical algorithm for mining frequent patterns. Apriori algorithm works on categorical attributes. Apriori uses breadth first searc

An Efficient Technique for mining Association rules using Enhanced Apriori Algorithm

There are Various mining algorithms of association rules. One of the most popular algorithm is Apriori that extracts frequent itemset from large database and getting the association rule for discovering the knowledge. this paper pitfalls the limitation of the original Apriori algorithm for wasting time for scanning the whole database searching on to the frequent itemsets, and presents an technique on Apriori by reducing that wasted time depending on scanning only some transactions whose support value is bigger than 25% of minimum Support is taken as frequent item set and is added to the frequent item sets and then rules are formed. An enhanced Apriori algorithm may find the tendency of a customer on the basis of frequently purchased item-sets The proposed algorithm is useful as a frequent item sets predictor with lower number of scans. DOI: 10.17762/ijritcc2321-8169.15063

Penerapan Metode Association Rule Mining untuk Asosiasi Ulasan Terhadap Aspek Tempat Wisata Jawa Timur Park 3

Google Review pada salah satu fitur Google Maps dapat menjadi salah satu media untuk mengukur tingkat kepuasan pengunjung Jawa Timur Park 3 (Jatim Park 3). Akan tetapi jumlah ulasan yang mencapai ribuan dan belum tersedianya media pengelola data ulasan dapat mempersulit manajemen Jatim Park 3 dalam mengeksplorasi dan menganalisis masukan pengunjung secara mendetail. Penelitian ini memanfaatkan teknik Association Rule Mining (ARM) dalam mengelola data ulasan sehingga dapat menemukan hubungan kata yang sering muncul pada ulasan. Teknik ini paling populer untuk menemukan hubungan tersembunyi antar variabel. Algoritma yang digunakan dalam mengimplementasikannya adalah algoritma Apriori karena dianggap paling efisien. Pada penelitian ini menggunakan data ulasan sebanyak 1067 ulasan dalam Bahasa Indonesia dari bulan Januari sampai bulan April tahun 2019. Berdasarkan wawancara, data tersebut digolongkan menjadi 8 aspek berdasarkan kata kunci yang sudah ditentukan sebelumnya. Aspek tersebut antara lain akses jalan, biaya, kebersihan, kepuasan, keramaian, pelayanan, keamanan, dan teknologi. Pengujian dilakukan untuk mengetahui pengaruh minimum support dan minimum confidence terhadap rule yang terbentuk. Keseluruhan aspek mampu menghasilkan asosiasi kata dengan algoritma Apriori. Selain itu, Keseluruhan rule yang terbentuk menghasilkan rata-rata lift ratio di atas 1 dimana rule dengan nilai lift ratio diatas 1 tersebut merupakan rule yang unik diantara rule-rule lain yang terebentuk dari asosiasi tersebut. Pada penelitian ini, rule yang terbentuk divisualisasikan untuk menampilkan keterkaitan antara kata kunci dengan aspek pada data ulasan pengunjung Jatim Park 3. Penelitian ini mencoba menggali informasi mengenai pemetaan layanan mana saja yang mendapatkan perhatian pengunjung di Jatim Park 3. Abstract Google Review, which is one of the features of Google Maps can be a medium to measure the satisfaction rate visitors of Jawa Timur Park 3 (Jatim Park 3). the number of reviews that reached thousands and media of review data manager is not available yet complicate the management of Jatim Park to explore and analyze visitor feedback in detail. The Association Rule Mining (ARM) technique is a text mining method that can support the knowledge discovery process in large document collections. ARM is able to link keywords to comments to find words that appear frequently. This technique is most popular for finding hidden relationships between variables. The algorithm used in this study is apriori algorithm because it is the most efficient. In this study, there are 1067 reviews of the visitors in Indonesian from January to April 2019 as the data. The data is classified into 8 aspects based on predetermined keywords. These aspects include road access, cost, cleanliness, satisfaction, hustle, service, security, and technology. Testing was conducted to determine the minimum support and minimum confidence impact of the established rules. The whole aspects is capable of generating word associations with an Apriori algorithm. In addition, the overall rules that are formed produce an average lift ratio above 1 where the rule with that value is a unique rule among other rules formed from the association. In this study, the rules that are formed are visualized to show the relationship between keywords and aspects of visitor reviews of Jatim Park 3. This research tries to dig up information about mapping which services get the attention of visitors in Jatim Park 3

Two new approaches to evaluate association rules

viii, 85 leaves : ill. ; 29 cmData mining aims to discover interesting and unknown patterns in large-volume data. Association rule mining is one of the major data mining tasks, which attempts to find inherent relationships among data items in an application domain, such as supermarket basket analysis. An essential post-process in an association rule mining task is the evaluation of association rules by measures for their interestingness. Different interestingness measures have been proposed and studied. Given an association rule mining task, measures are assessed against a set of user-specified properties. However, in practice, given the subjectivity and inconsistencies in property specifications, it is a non-trivial task to make appropriate measure selections. In this work, we propose two novel approaches to assess interestingness measures. Our first approach utilizes the analytic hierarchy process to capture quantitatively domain-dependent requirements on properties, which are later used in assessing measures. This approach not only eliminates any inconsistencies in an end user’s property specifications through consistency checking but also is invariant to the number of association rules. Our second approach dynamically evaluates association rules according to a composite and collective effect of multiple measures. It interactively snapshots the end user’s domain- dependent requirements in evaluating association rules. In essence, our approach uses neural networks along with back-propagation learning to capture the relative importance of measures in evaluating association rules. Case studies and simulations have been conducted to show the effectiveness of our two approaches