Opinion mining on large scale data using sentiment analysis and k-means clustering

Sales data analysis is an important aspect of modern businesses, as it enables them to gain insights into customer preferences and behavior, optimize marketing strategies, and improve revenue. With the rise of big data and machine learning, businesses can now leverage these tools to analyze sales data and gain even deeper insights. This paperfocuses on analyzing sales data using machine learning algorithms, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm. KNN is a popular machine learning algorithm used in classification and regression problems, and it works by identifying the K closest data points to a new observation and predicting its class or value based on the majority class or average value of its nearest neighbors. The dataset used in this paperis from a retail store and includes information on product type, sales channel, customer demographics, and sales volume over a period of several years. The paper will use Python and various machine learning libraries to perform data preprocessing, exploratory data analysis, feature engineering, and modeling. Specifically, KNN will be used to develop a predictive model for sales forecasting, which can help businesses optimize their inventory and production planning. The paper will also use clustering techniques, such as K-Means, to identify customer segments based on their purchasing behavior and demographics, and association rule mining to identify product affinities and recommend complementary products. The results of this analysis can help businesses gain a deeper understanding of their sales data and make data-driven decisions to improve their bottom line. By leveraging the power of KNN and other machine learning algorithms, businesses can gain insights into customer behavior and preferences, optimize their marketing strategies, and improve their overall revenue. Keywords: KNN Algorithm, Data Analytics, Machine Learning, Support Vector Machine.

Mining association rules is one of the most important tasks for uncovering hidden patterns in datasets to aid organizations in making efficient decisions based on customer behavior and preference. This association rule identifies other possible connections that may exist beyond customer preference and behavior. Some of the most prominent algorithms used in this regard are Apriori algorithms and FP-Growth algorithms. They help to obtain sufficient information based on huge datasets. However, association rule mining using existing algorithms has encountered several problems. They consume a huge memory space for data processing. They need data searching for finding out the frequency of each item. They also establish rules with low efficiency. In overcoming such problems engendered by data mining association rules, a better efficient Fp-TDA algorithm using advanced FP-Growth algorithms has recently been developed. This algorithm uses Matrix Tree instead of a tree concept used in existing algorithms. This largely cuts down data processing time. This concept largely cuts down frequently generated data. This concept largely addresses precision. This largely enhances data processing. This concept largely addresses efficiency. This largely addresses data quality. This largely addresses data time. This concept largely addresses memory space. The Fp-TDA largely addresses problems in association rule mining. This concept largely addresses data processing. This concept largely addresses data quality. This concept largely addresses data memory

An improved understanding of customer preference is crucial for successful business in physical stores. Online stores are capable learning customer preference from the click logs and transaction records, while retailers with physical store still lack effective methods to in-depth understand customer preference. Fortunately, user-generated data from mobile devices and social media are providing rich information to uncover customer preference. In this paper, we present a novel approach to mine customer preference in physical stores from their interaction behaviors. To demonstrate the utility of the proposed model, we conduct a store-type recommendation model for physical stores by jointly considering the learned customer preference and temporal influence. We have performed a comprehensive experiment evaluation on two real-world data sets, which are collected by more than 120 000 customers during 12 months from two urban shopping malls. Experimental results show the superiority of the proposed model not only in recommending interesting stores for customer, but also help retailers better understand customer preference.

With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centres is used to initialise arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.

<span>In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.</span>

Online advertising, one typical application of recommendation system, calls for effective and accurate recommendations of keywords. Extreme sparse and large scale data makes online advertising a challenging problem. To achieve better performance and accuracy of the recommendation, a better model with a short turnaround time is needed. In this paper, we address the problem of personalized online advertising for extreme sparse and large scale data. We develop a novel machine learning model (Similarity Powered Pairwise Amplifier Network, SPPAN for short). The complexity of this model (a.k.a. the number of parameters) grows with the amount of observed data, which makes it suitable to extremely sparse data. The training algorithm based on gradient descent makes it easy to parallelize. The similarity model combines the user neighborhood and item neighborhood ideas in collaborative filtering smartly, obtaining a cost-effective way to handle large scale data. The proposed framework is evaluated on a large set of real-world data set from a large internet company (expressed by Company A). The experiment results demonstrate that the proposed SPPAN model can greatly improve the prediction and recommendation accuracy on that extreme sparse data set compared with existing approaches.

Augmenting feature model through customer preference mining by hybrid sentiment analysis

Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc. The primary approach for this type of detection in previous researches is based on principal score (PS) of divided batches or sliding windows by computing top eigenvalues of the correlation matrix, e.g. the Lanczos algorithm. However, this paper brings up the phenomenon of principal score degeneration for large data set, and then mathematically and practically prove current PS-based methods are likely to fail for CAD on large-scale streaming data even if the number of correlated anomalies grows with the data size at a reasonable rate; in reality, anomalies tend to be the minority of the data, and this issue can be more serious. We propose a framework with two novel randomized algorithms rPS and gPS for better detection of correlated anomalies from large streaming data of various correlation strength. The experiment shows high and balanced recall and estimated accuracy of our framework for anomaly detection from a large server log data set and a U.S. stock daily price data set in comparison to direct principal score evaluation and some other recent group anomaly detection algorithms. Moreover, our techniques significantly improve the computation efficiency and scalability for principal score calculation.

Frequent Itemset Mining is one of the most investigated fields of data mining. It is expensive to mine frequent itemsets for a large scale data set. Especially when some data is added into the data set, it is still time-consuming from the scratch to re-compute the complete data set to update the frequent itemsets of the data set. Aiming to improve the performance of frequent itemset mining for large scale and dynamic data set, we propose a new incremental Apriori algorithm based on Spark. It reuses existing results from previous computation to modify the frequent itemsets according to the newly added data, which avoid massive recomputation. The newly proposed algorithm also takes full advantage of distributed resources with the support of Spark. Analysis in theory ensures that the newly proposed algorithm is correct. Experiments on the real-world data set demonstrate that the newly proposed algorithm effectively avoids reduplicated computation and improves the performance of frequent itemset mining with no additional storage overhead.

The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.KeywordsMachine LearningData MiningWEKAClassificationBioinformatics

Deriving customer preferences for hotels based on aspect-level sentiment analysis of online reviews

In the digital era, the rapid growth of social media and online platforms has led to an explosion of unstructured textual data that holds significant business value. Traditional marketing strategies, once reliant on structured data such as demographics and purchase history, now benefit from insights derived from text analytics and sentiment analysis. This paper explores the integration of structured and unstructured data to strengthen marketing intelligence and customer segmentation. By utilizing text mining techniques and Natural Language Processing (NLP), unstructured data such as customer reviews and comments can be analyzed to extract sentiments, identify emerging trends, and refine customer relationship strategies. The study proposes an integrated framework that combines data extraction, transformation, and loading (ETL) processes with a data warehouse system for unified analysis. Using clustering algorithms such as K-Means and visualization tools, insights into customer behavior, preferences, and market segmentation are revealed. The paper also discusses the challenges of handling multilingual and context-dependent text, ethical and privacy considerations, and the technical architecture necessary for business intelligence implementation. Findings suggest that effective integration of textual analytics with structured data can lead to more informed decision-making, improved marketing strategies, and stronger customer engagement.

Chapter 20 - Query Suggestion with Large Scale Data

Individualized boosting learning for classification

Advances in mass spectrometry-based proteomics have yielded a substantial mapping of the tyrosine phosphoproteome and thus provided an important step toward a systematic analysis of intracellular signaling networks in higher eukaryotes. In this study we decomposed an uncharacterized proteomics data set of 481 unique phosphotyrosine (Tyr(P)) peptides by sequence similarity to known ligands of the Src homology 2 (SH2) and the phosphotyrosine binding (PTB) domains. From 20 clusters we extracted 16 known and four new interaction motifs. Using quantitative mass spectrometry we pulled down Tyr(P)-specific binding partners for peptides corresponding to the extracted motifs. We confirmed numerous previously known interaction motifs and found 15 new interactions mediated by phosphosites not previously known to bind SH2 or PTB. Remarkably, a novel hydrophobic N-terminal motif ((L/V/I)(L/V/I)pY) was identified and validated as a binding motif for the SH2 domain-containing inositol phosphatase SHIP2. Our decomposition of the in vivo Tyr(P) proteome furthermore suggests that two-thirds of the Tyr(P) sites mediate interaction, whereas the remaining third govern processes such as enzyme activation and nucleic acid binding.